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Impression & Pattern Evidence – Forensic Technology Center of Excellence

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Impression & Pattern Evidence

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• All Impression & Pattern
• Bloodstain Pattern Analysis (25)
• Field Devices (9)
• Firearm & Toolmarks (45)
• Footwear & Tire (24)
• Friction Ridge (43)
• Microscopy (12)
• Odontology (12)
• Questioned Documents (11)

2024 National Forensic Science Week

2024 NIJ Forensic Science Graduate Research Symposium

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Virtual Workshop Series: 3D Firearm Imaging

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2024 NIJ Forensic Science R&D Symposium

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2024 ASCLD Emerging Issues Webinar Series

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2023 Graduate Research Symposium and Poster Session

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2023 National Forensic Science Week Teacher How-To Guides

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2023 NIJ Forensic Science R&D Symposium

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ASCLD Train the Director Webinar Series

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Evaluation of Purdue University’s 3D Imaging Prototype for Footwear and Tire Impressions

2022 National Forensic Science Week FTCOE Student Research Poster Session

2022 National Forensic Science Week Murder Mystery Event

2022 National Forensic Science Week Ask an Expert

2022 National Forensic Science Week

Just identifying decedents through postmortem prints.

2022 Update: 3D Imaging Technologies and Virtual Comparison Microscopy for Firearms Examination, A Landscape Study

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Human Factors in Forensic Science Practice

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Terrestrial LiDAR Scanners: Guidelines for Use in Criminal Justice Applications

Guidelines for the Use of Terrestrial LiDAR Scanners in Criminal Justice Applications

Human Factors in Forensic Science Practice Sourcebook

2022 nij forensic science r&d symposium.

U.S. National Footwear Database System Feasibility Study

A Unique Approach to a Crime Gun Intelligence Center with the Inclusion and Support of 3D Virtual Comparison Technologies

2022 Firearm and Toolmarks Policy and Practice Forum

2021 Year in Review

The Effect of Time on Rusted Firearm Identification

Just advanced capabilities in firearm and toolmark analysis.

Success Story: LatentSleuth: A Case Study on the Impact of Federal R&D Funding

Two-Pronged Study of Bullets Fired by Consecutively Rifled Barrels

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Exploration of Breech Face Subclass Characteristics 

Firearm and Toolmarks Webinar Series

FLN-TWG: 3D Imaging for Firearms and Toolmarks

2021 Forensic Science Research Federal Stakeholders Public Meeting

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2021 national forensic science week murder mystery event, 2021 national forensic science week practitioner interviews, 2021 national forensic science week, 2021 national forensic science week ftcoe student research poster session.

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 just the impression and pattern/trace evidence portfolio.

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2021 nij forensic science r&d symposium.

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2020 Year in Review

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2020 SWAFS Virtual Conference

Just psychopathy and criminal behavior.

Terrestrial LiDAR Scanning Working Group for Criminal Justice Applications, First Meeting Report

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Conference Proceedings: 2018 Impression Pattern and Trace Evidence Symposium

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2018 iptes – impression & pattern breakout day 2 am – part 2, 2018 iptes – impression & pattern breakout day 2 am – part 1, 2018 iptes – impression & pattern breakout day 1 pm, 2018 iptes plenary – statistics & testimony from practitioner, just case studies: two murders, one trace fiber, just case studies: from teeth to trafficking, just case studies: a gruesome murder in mesa, just case studies: atlanta olympic bombing.

Bloodstain Pattern Analysis on Textiles: A Technology Transition Workshop

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2015 NIJ Forensic Science R&D Symposium

Forensic optical topography working group.

2016 TechBeat Articles Featuring the FTCOE

A landscape study of forensic optical topography, a validation and evaluation of magneto-optical imaging technology, forensic optical topography working group meeting, an evaluation and utility assessment of magneto-optical sensor technology, a landscape study of mobile id fingerprint devices, understanding basic statistical concepts: fingerprints, the emperor’s new clothes: a guide to latent print testimony, swipes, wipes and transfer impressions, r&d 2014 – fundamental forensic science research-part ii, r&d 2014 – fundamental forensic science research – part i, shooting reconstruction: 4 elements of trajectory, new paradigm for fingerprint reporting, magneto-optical sensor roundtable, latent fingerprints: reducing erroneous exclusions, latent fingerprints: developing methods and new technology, fluorescence of blood impressions with acid yellow, fingerprint identification: reliability and accuracy, false-positive and false-negative error rates in cartridge, error & uncertainty in bloodstain pattern analysis, demystifying vacuum metal deposition [archival], collecting footwear and tire impressions in snow, bloodstain documentation and collection methods, ascld webinar series: latent prints, ascld webinar series: firearms, a general framework for the estimation of likelihood ratios, 2017 nij r&d series impression, pattern, & trace evidence, 2016 nsaps panel 2 – victim-centered approaches, 2016 nij r&d series impression, pattern & trace evidence, 2014 techbeat articles featuring the ftcoe.

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The Forensics Library

Impression Evidence

Impression evidence includes any markings produced when one object comes into contact with another, leaving behind some kind of indentation or print. Such evidence encountered includes footwear impressions, tyre marks, and markings created by tools and similar instruments.

Footwear Impressions Whenever an individual takes a step, a footwear impression may potentially be left behind on the surface. Such an impression may be two-dimensional, the print left behind on a flat surface in some deposited material, or three-dimensional, formed in a soft surface such as soil. Numerous techniques are available for the enhancement and recovery of footwear impressions, though non-destructive methods should always be employed first if possible.

Two-dimensional impressions can often be treated in a similar way as fingerprints. The gentle application of a fine powder may develop footprints on flat surfaces. Certain chemicals and dyes may enhance impression on surfaces such as glass or tile. However paper and similar porous surfaces will simply absorb such chemicals, rendering the impression useless. The application of alternative light sources can enhance two-dimensional footwear impressions. The light source should be positioned to give a low angle of incident light, creating shadows to provide a contrast.

One of the more common methods of recovering three-dimensional impressions is to create a cast of the impression, usually using plaster of Paris, dental stone, or a similar casting material. The plaster is mixed with an appropriate amount of water and gently poured into the impression. Once set, it can be removed and taken for examination and comparison purposes.

Impressions in dust are obviously extremely delicate, though can be carefully recovered using electrostatic treatment. An electrostatic lifter passes a voltage across a thin layer of conductive film, which is composed of a lower layer of black insulating plastic with an upper layer of aluminium foil. The electrostatic charges cause particles of the impressions to jump onto the black underside, recovering the dust impression. As dental stone emits heat as it sets, it is evidently not suitable for casting impressions in snow. In this instance aerosol products exist, such as Snow Impression Wax. This is applied to the impression numerous times at intervals of one to two minutes and then left to dry. The impression can then be cast as normal. Alternatively flour sulphur may be used to cast snow prints. This is boiled to produce a hot casting compound which, upon contact with the cold snow, solidifies to produce a detailed cast.

Any footwear impressions collected from the crime scene may be useless unless there are suspect samples available for comparison. By applying a film of light oil to the undersole of a shoe and pressing it into a sheet of oil-impregnated foam rubber, a test impression can be produced. Alternatively the undersole is oiled and pressed onto plain white paper, which is then dusted with fine black powder similar to that used to develop latent prints. If a three-dimensional impression is to be obtained, it should, if possible, be produced using the same methods and mediums as the original impression.

Even if no other samples are available for comparison, a recovered shoe impression may yield a vast amount of information. Almost all items of footwear will bear an undersole with distinctive patterns, which manufacturers are increasingly designing to be specific to them. In some locations such patterns have been stored in databases for comparison purposes. Though these patterns are identical for the same brand and type of shoe, a certain degree of individuality may be imparted from the manufacturing process or general wear. As a shoe is worn certain details fade in different places, depending on the weight and walk of the wearer, and specific damages may be caused. The size of the shoe, which may easily be obtained by examining the recovered impression, may prove useful, though not as a positive identifier.

Tyre Impressions As vehicles may be present at crime scenes, before, during or after the crime, tyre impressions may be discovered at the scene, usually left behind in soil. The enhancement and collection of these is similar to that of footwear impressions. If a tyre impression is discovered at a scene the impression corresponding to the opposite tyre should also be searched for, as the distance between these may provide further information regarding the vehicle in question.

Instrument Marks Instruments and tools used during a crime will often leave marks behind at the scene, which may prove beneficial in establishing links between a particular object and the scene. Common instruments encountered fall into two categories; cutting instruments and levering instruments. Common cutting instruments include knives, bolt croppers and drills, with screwdrivers and jemmies being common levering tools. Such instruments will often suffer severe damage when used, giving them characteristic features which may leave behind a distinctive impression at the scene. A cast can be made of the impression at the scene, usually using a type of silicon rubber. This can then be used in comparison with other impressions or instruments to establish a match and determine which tool was used. The cast itself will be a negative of the original mark, and so should not be directly compared with the suspected tool. Instead the suspected instrument can be used to make a number of test marks in a similar medium.

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How Impression Evidence Works

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Fingerprints , along with the magnifying glass and the microscope are one of the most iconic and recognizable images connected to crime scene investigations and forensic science. The opening credit montages for many television crime dramas often superimpose large, ominous details of fingerprints over shots of the main characters. The old logo for the crime network TruTV, formerly known as Court TV, featured a prominent image of a fingerprint­. This form of evidence has become a metaphor for uniqueness, an important concept in forensic science.

Forensics Image Gallery

­With more sophisticated technology such as DNA profiling, experts are finding that fingerprints aren't as perfect a system as we'd like to think. Despite the importance of fingerprinting and its useful practice, some actually argue against its supposed infallibility: Some courts have even overturned cases because of bad fingerprint matches, including a 1998 murder trial in which a Delaware man was wrongfully convicted and spent two years in prison [source: NY Times ].

Fingerprints aren't the only things a suspect can leave at the scene of a crime. One of the most influential philosophies behind modern forensic science, commonly known as Locard's exchange principle , states that "with contact between two items, there will be an exchange." The late chemist and forensic scientist Paul L. Kirk elaborates:

­Another option investigators have when examining a crime scene is impression evidence , an important and sometimes overlooked aspect of the criminal investig­ation process. What do forensics experts look for when they investig­ate impression evidence? How do they preserve impression evidence, and what can it tell them about a crime scene? To find out how forensic scientists make an impression on the jury,

Please copy/paste the following text to properly cite this HowStuffWorks.com article:

Utilizing Impression Evidence in Crime Scene Reconstruction

by Det. F.D. Zigan, CLPE, CCST, MCSFS, Crime Scene Investigations, Polygraph Examiner, Roswell (Ga.) Police Department

One of the many things I love about being able to work as a crime scene investigator and latent fingerprint examiner is that I get to travel to conferences, visit other agencies, and attend various schools. One common theme I see is separation of the disciplines within the arena of crime scene investigation, and the specialization of those disciplines which sometimes further separates them.

A smaller agency may only have a few crime scene technicians. Those technicians then grow within their career and specialize in different areas. One may become a latent fingerprint examiner, another may become a bloodstain subject matter expert, or shooting incident reconstructionist, but all still have to process crime scenes.

Larger agencies sometimes have a separate ID section that only analyze and compare various impression evidence, while the crime scene technicians process the scenes and document evidence within the lab. I have seen case agents or lead detectives directing the crime scene technician on what to process, take for evidence, and in some cases what to photograph. The detectives then try to utilize the information provided to formulate a reconstruction of events themselves. I have heard crime scene technicians state, “it is not my job to formulate an opinion on what happened at the scene, I just process it for evidence, collect the evidence, and write a report on what I observed and collected”. Unfortunately those observations don’t lead to a possible explanation/ hypothesis as to why or how that evidence came to be there.

There is a growing disconnect within the disciplines of what is being called crime scene processing and crime scene reconstruction. Many are overlooking or ignoring the importance of the context in which impression evidence is found. I have personally seen this play out during training classes, on scenes, and at other agencies that have requested outside assistance.

The crime scene investigator should not only be searching for impression evidence, but also observing and documenting the context in which it was found.

“Finding a fingerprint at the scene may be important, but of greater importance is the context in which we find the fingerprint. “ Bevel Gardner

Merely documenting where a friction ridge impression is found and making a same source conclusion is not sufficient enough for an investigation. The only information that has been obtained is that a particular person has touched an item at some point in time. The orientation, placement, and distortion must also be documented.

Orientation and placement is easy to document, however, understanding the distortion in a friction ridge pattern can be challenging. Understanding distortion in a friction ridge pattern can explain how an item or substrate was touched. It can also help the examiner articulate changes in the pattern such as minutiae displacement, ridge endings that now appear as bifurcations, differences in ridge counts in same source conclusions, change in insipient size and shape, and the change in appearance of secondary creases and wrinkles to name a few.

If the friction ridge pattern has been documented properly by the responding Crime Scene Investigator, then a reconstruction of the scene can now be completed properly.

In a real case example, a subject was accused of looking into car vehicle windows and attempting to pull on car door handles to see if they were unlocked. See Figure 1.

Instead of the crime scene investigator simply notating that a friction ridge pattern was located on the car window and classifying it as the Hypothenar area, the description could be more involved and state that the impression was cupped which is consistent with a person trying to shield the glare on the window so they could see in the vehicle better. There is also no real discernable lateral distortion (directional movement) which indicates that the hand was held relatively still. This is obviously not an accidental touch as some attorney’s claim. An overall photograph of the above friction ridge pattern would help the investigator put this into proper perspective as well. Taking only close ups of friction ridge evidence can be detrimental to the case if the investigator, attorneys, and juries can’t see its original location.

Another example is the simple shutting of a car door. See figure 2.

It is not hard to discern that it is highly improbable that this impression was deposited with the door closed. Why not articulate that in the report instead of just stating the impression was recovered on the car door? Another example of impressions or fingermarks being deposited while shutting a vehicles door is shown in Figure 3.

Understanding the distortion and how the impression evidence is oriented plays a vital role in either substantiating or refuting a statement given by a suspect, victim or witness.

Example: The victim stated that the suspect held a beer bottle over his head and threatened to hit the victim with it. The suspect denies the allegation and states that he only held the beer bottle normally like he would be when drinking it. A friction ridge pattern is found inverted on the neck of the bottle. See Figure 4. While this is not conclusive proof as there are multiple scenarios that could exist in which this pattern was deposited, it does contradict the statement of the suspect and can now be addressed in an interview.

Here is another example of impression evidence helping to construct a scene. The suspect stated that the female victim was not passed out from alcohol and she got in the vehicle of her own free will. Witnesses stated they observed an unconscious female being placed on the hood of the suspect’s vehicle while he opened the door to the vehicle. See Figure 4. Upon closer inspection of the impressions left on the vehicle in figure 5, hair impressions could be observed in the center of the hood, non-friction ridge skin could be observed where the approximate location of the arms would be with what appeared to be wrist impressions, a void where the victim’s shirt was located, and more non-friction ridge skin impressions where the victim’s lower back would be could be seen.

There are many more examples. So what are some of the details within the distortion of friction ridge patterns that should be understood to help put the impression evidence in context? Some of the clues that one should have a basic understanding of, is being able to recognize movement and directional indicators. One such clue is the noise between ridge marks where the furrows should be. Any noise in the furrows that is not attributed to the substrate, is usually an indicator that some movement has taken place. For examiners, this is a red flag that should prompt closer observations and the possibility of the displacement of ridge characteristics. Another indicator of movement would be the buildup of surface debris and matrix on one side of the ridge. The side with the buildup generally signifies the direction of movement. Other indicators are the compression and expansion of ridge spacing. The area with expansion would signify the direction of movement with the compression representing the trailing side. See. Figure 6 of friction ridge pattern moving in a downward motion.

Why is it important to know these indicators? The difference between being able to say the crime scene investigator located a friction ridge pattern on the exterior window of a home that the suspect stated he just looked into, versus being able to state that the suspect’s intent was to actually manipulate or open the window. See figure 6 for a case sample of an impression showing upward movement.

A discernable expansion of the ridges are observed in the upper portion of the friction ridge pattern in Figure 7 along with compression in the lower portion. There is also a buildup of matrix on the side of the ridges in the direction of the movement.

I asked Zack Kowalske a certified Crime Scene Reconstructionist through the IAI (International Association for Identification), why he thought there was a growing disconnect between the crime scene investigator, and the Crime Scene Reconstructionist. His answer was, “Because the disciplines of crime scene investigation and forensic science have evolved with such depth in their own right, a loss of translational knowledge and context has occurred. The over specialization of disciplined practitioners can at times overlook the fundamentals of the crime scene as a whole.”

So on one hand, we have some crime scene investigators who are trained to document and collect evidence only. We have latent fingerprint examiners receiving fingerprint cards and macro photographs of fingerprints whose sole job is to analyze and compare fingerprints. We have technicians processing evidence in the laboratories, and we have crime scene reconstructionists trying to make sense out of everything that was collected and documented separately.

The crime scene investigator has been trained in crime scene photography. They were taught to take overall photographs, then photographs with the evidence in context, then close up photographs of the evidence with scale. This should be done for impression evidence as well. There seems to be a disconnect between documenting physical evidence and impression evidence. It should be photographed exactly the same way. The crime scene report, when processing an item at the scene, should include the orientation of the impression as well as any distortion observed prior to lifting the print, as the process of physically lifting a print changes the print.

The crime scene reconstructionist should also be familiar with or have a basic understanding of friction ridge patterns and how distortion affects them. This would greatly help them understand the dynamics within a scene which could strengthen the opinion of the case. The reconstructionist needs this knowledge as a system of checks and balances. They cannot only rely on another’s interpretation of the distortion.

The crime scene investigator needs to understand that the roles within this job are changing as we understand more about the meanings of distortion found within a friction ridge pattern. So their methods of documenting must change and reflect that. They also need more training regarding fingerprint interpretation and how distortion plays a role in context.

How do we fix this disconnect?

The answer is not an easy or seamless one. There are also problems with inserting an opinion on how or why an impression came to be formed. Some of these issues are:

• Subjectivity: Determining the context in which a fingerprint was located requires interpretation, and different experts may have different opinions on what the context means. This subjectivity can make it difficult to arrive at a clear and objective conclusion.
• Incomplete information: An expert may not have all the relevant information to accurately determine the context in which a fingerprint was located. For example, if the crime scene was not thoroughly examined or documented, important contextual clues may be missed.
• Lack of standards: There are no universally accepted standards or guidelines for interpreting the context in which a fingerprint was located. This can make it difficult to compare and evaluate different expert opinions.
• Bias: Experts may have biases or preconceived notions about the case or the individuals involved, which can affect their interpretation of the context in which the fingerprint was located.
• Complexity: The context in which a fingerprint was located can be complex and multifaceted, involving multiple variables such as lighting conditions, surface texture, and the presence of other evidence. Interpreting these variables and determining their significance can be challenging.

It is now incumbent for the crime scene reconstructionist and the crime scene investigator to understand what role the context of impression evidence plays in an investigation. Being aware and understanding that that issues listed above exist and more crossover training to further understand what each job needs might be a good first step. However, I believe awareness that fingerprints can offer more information than just the identification of the individual that left ii is also key.

References:

Bevel, T., & Gardner, R. M. (2019). Bloodstain pattern analysis with an introduction to crime scene reconstruction (4th ed.). CRC Press. DeRonde, A; Kokshoorn, Bas; DePoot, C; DePuit, M. The Evaluation of Fingermarks given Activity Level propositions. Forensic Sci Intl 2019; 302:109904. http://dx.doi.org/10.1016/j.forsciint.20190109904

Dror, I. E., & Charlton, D. (2006). Why experts make errors. Journal of Forensic Identification, 56(4), 600-616.

Dror, I. E. (2016). Emotions get the better of us: The role of emotion in forensic science. Australian Journal of Forensic Sciences, 48(1), 1-16

https://www.nist.gov/organization-scientific-area-committees-forensic-science/friction-ridge-subcommittee

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Ted Bundy Although serial killer Ted Bundy was responsible for an estimated 30-plus murders, there was little physical evidence to connect him to the crimes when he was arrested in 1975. Two years later, having been convicted only of kidnapping, Bundy was preparing to stand trial for murder in Colorado when he escaped and headed to Florida. There, he killed three more people early in 1978, and when he was finally captured in February of that year, the physical evidence in those cases led to his conviction. Most crucial was the matching of a bite mark on the buttock  of victim Lisa Levy to the Bundy’s distinctive, crooked and chipped teeth. He was convicted also of the murder of 12-year-old Kimberly Leach based on fibres found in his van that matched the girl’s clothing. Bundy was put to death in 1989.

The Lindbergh Kidnapping On March 1, 1932, Charles Lindbergh Jr., the 20-month-old son of the famous aviator, was kidnapped, and although a ransom of $50,000 was paid, the child was never returned. His body was discovered in May just a few miles from his home. Tracking the circulation of the bills used in the ransom payment, authorities were led to Bruno Hauptmann, who was found with over $14,000 of the money in his garage. While Hauptmann claimed that the money belonged to a friend, key testimony from handwriting analysts matched his writing to that on the ransom notes . Additional forensic research connected the wood in Hauptmann’s attic to the wood used in the make-shift ladder that the kidnappers built to reach the child’s bedroom window. Hauptmann was convicted and executed in 1936.

The Atlanta Child Murders In a two year period between 1979 and 1981, 29 people — almost all children — were strangled by a serial killer. Police staked out a local river where other bodies had been dumped and arrested Wayne Williams as he was driving away from the sound of a splash in an area where a body was recovered a couple of days later. Police didn’t witness him drop the body, so their case was based largely on forensic evidence gathered from fibers found on the victims . In all, there were nearly 30 types of fiber linked to items from Williams’ house, his vehicles and even his dog. In 1982, he was convicted of killing two adult victims and sentenced to life in prison, although the Atlanta police announced that Williams was responsible for at least 22 of the child murders.

The Howard Hughes Hoax  In 1970, authors Clifford Irving and Richard Suskind concocted a scheme to forge an autobiography of notoriously eccentric and reclusive billionaire Howard Hughes. Assuming that Hughes would never come out from hiding to denounce the book, they felt that their plan was fool-proof. Irving went to publisher McGraw-Hill claiming that Hughes had approached him to write his life story and that he was willing to correspond with only the author. As proof, Irving produced forged letters that he claimed were from Hughes. McGraw-Hill agreed, paying $765,000 for the right to publish the book. When word of the book was made public, however, Hughes contacted reporters to denounce it as false. Not wishing to appear in public, the billionaire would talk to reporters only via telephone. Thus, a “spectographic voiceprint analysis,” measuring tone, pitch and volume, was conducted to determine if the speaker was indeed Howard Hughes. Although a handwriting expert had previously been fooled by the notes that Irving had forged, the voice analyst correctly identified the speaker as Hughes. Irving was exposed and confessed before the book was published. He spent 17 months in prison, while Suskind spent five. Irving later wrote a book about the scheme,  The Hoax , which became a major motion picture in 2008.

The Night Stalker   Between June 1984 and August 1985, a Southern California serial killer dubbed the Night Stalker broke into victims’ houses as they slept and attacked, murdering 13 and assaulting numerous others. With citizens on high alert, an observant teenager noticed a suspicious vehicle driving through his neighborhood on the night of August 24, 1985. He wrote down the license plate and notified police. It just so happened that the Night Stalker’s latest attack took place that night in that area, so police tracked down the car. It had been abandoned, but police found a key piece of evidence inside: a fingerprint . Using new computer system, investigators quickly matched the print to 25-year-old Richard Ramirez and plastered his image in the media. Within a week, Ramirez was recognized and captured by local citizens. He was sentenced to death.

Machine Gun Kelly George “Machine Gun” Kelly was a notorious criminal during the Prohibition era, taking part in bootlegging, kidnapping and armed robbery. On July 22, 1933, he and another man kidnapped wealthy Oklahoma City oilman Charles Urschel. After a series of ransom notes and communications, a $200,000 ransom was paid — the largest amount ever paid in a kidnapping to date. Urschel was released nine days later, unharmed. The oilman had shrewdly paid close attention to every detail during his ordeal and was able to relate it all to police. Although he was blindfolded, he could tell day from night and was able to estimate the time of day that he heard airplanes fly above. He also noted the date and time of a thunderstorm and the types of animals he heard in what he presumed to be a farmhouse. Using his memories, the FBI pinpointed the likely location in which Urschel was held to a farm owned by Kelly’s father-in-law. What truly linked Kelly and his gang to the kidnapping, though, was Urschel’s fingerprints , which he made sure to place on as many items in the house as possible. Kelly was sentenced to life in prison, where he died in 1954.

The Green River Killer The Green River Killer was responsible for a rash of murders — at least 48 but possibly close to 90 — along the Green River in Washington state in the ’80s and ’90s. Most of the killings occurred in 1982-83, and the victims were almost all prostitutes. One of the suspects that police had identified as early as 1983 was Gary Ridgway , a man with a history of frequenting and abusing prostitutes. However, although they collected DNA samples from Ridgway in 1987, the technology available didn’t allow them to connect him to the killings. It wasn’t until 2001 that new DNA techniques spurred the reexamination of evidence that incriminated Ridgway . He was arrested and later confessed. Ridgway pleaded guilty to 48 murders — later confessing to even more, which remain unconfirmed — in exchange for being spared the death penalty. He was sentenced to 48 life sentences without the possibility of parole.

BTK Killer The BTK (“Bind, Torture, Kill”) Killer was a serial killer who terrorized the Wichita, Kansas area between 1974 and 1991, murdering 10 people over the span. The killer craved media attention and sent letters to local newspapers and TV stations, taunting investigators. It’s this egotism that led to his capture, however. When he resurfaced in 2004 with a series of communications, he chose to send a computer floppy disk to the  Wichita Eagle . Forensic analysts traced the deleted data on the disk to a man named Dennis at the Christ Lutheran Church in Wichita . It didn’t take long for the police to arrest Dennis Rader , who confessed and was sentenced to nine life terms in prison.

Jeffrey MacDonald Early in the morning of February 17, 1970, the family of Army doctor Jeffrey MacDonald was attacked, leaving the doctor’s pregnant wife and two young daughters dead from multiple stab wounds. MacDonald himself was injured by what he claimed to be four suspects, but he survived with only minor wounds. Doubt was immediately cast on the doctor’s story, based on the physical evidence on the scene that suggested that he was the killer. However, the Army dropped the case because of the poor quality of the investigative techniques. Several years later, though, MacDonald was brought to trial in a civilian court. Key evidence was provided by a forensic scientist who testified that the doctor’s pajama top, which he claimed to have used to ward off the killers, had 48 smooth, clean holes — too smooth for such a volatile attack. Furthermore, the scientist noted that if the top was folded, the 48 holes could easily have been created by 21 thrusts — the exact number of times that MacDonald’s wife had been stabbed. The holes even matched the pattern of her wounds, suggesting that the pajama top had been laid on her before during the stabbing and not used in self-defense by the doctor. This crime scene reconstruction was crucial in MacDonald’s conviction in 1979. He was sentenced to life in prison for the three murders.

John Joubert In 1983, two murders of schoolboys rocked the Omaha, Nebraska area. The body of one of the boys was found tied with a type of rope that investigators couldn’t identify. While following up on the lead of a mysterious man scouting out a school, they traced the suspect’s license plate to John Joubert , a radar technician at the local Air Force base. In his belongings, they found a rope matching the unusual one used in the murder (which turned out to be Korean). Although DNA analysis technology was not yet an option, the extreme rarity of the rope was enough to lead to Joubert’s confession . Furthermore, hair from one of the victims was found in Joubert’s car . The child killer was even linked to a third murder, in Maine, when his teeth were found to match bite marks on a boy killed in 1982. Joubert was found guilty of all three murders and was put to death in the electric chair in 1996.

Source: Criminal Justice School

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Bloody shoeprints are visible on tile flooring. (Courtesy of John Black, Ron Smith & Associates)

A plastic print is a three-dimensional impression left on a soft surface. This includes shoe or tire tracks left in sand, mud or snow.

Plastic shoeprint left in sand. (Courtesy of Aubrey Askins, Tacoma Police Department)

A latent print is one that is not readily visible to the naked eye. This type is created through static charges between the sole or tread and the surface. Examiners or investigators use powders, chemicals or alternate light sources to find these prints. Examples include shoeprints detected on a tile or hardwood floor, window sill, or metal counter, or tire tracks detected on road surfaces, driveways or sidewalks.

Dust impression left on a masonite surface, illuminated with oblique lighting. (Courtesy of Scott Campbell, Ron Smith & Associates)

How Samples are Collected

Examiners use several methods for collecting footwear and tire track evidence depending on the type of impression found. For impressions in soil, snow or other soft surfaces, casting is the most commonly used collection method. For imprints, examiners generally try to collect the entire object containing the imprint, such as a whole sheet of paper or cardboard with a shoe print. When that is not possible, for instance, if the print is on a bank counter, the examiner would use a lifting technique to transfer the imprint to a medium that can be sent to the laboratory.

Casts are created of footwear impressions to preserve them and allow for comparison and analysis. (Courtesy of NFSTC)

As with any evidence found at a crime scene, shoeprints and tire tracks must be properly documented, collected and preserved in order to maintain the integrity of the evidence. Impression evidence is easily damaged, so steps must be taken to avoid damage to the evidence. This includes securing and documenting the scene prior to collecting any evidence.

In the case of impression evidence, general photographs of the evidence location in relation to the rest of the scene are taken, along with high-resolution images of the individual imprints or impressions. Examiners may use alternate light sources or chemical enhancers to capture as much detail as possible, especially with latent imprints.

Properly photographing impressions is crucial. Since there is only a slight difference between different shoe sizes, if the photographs are not taken at a 90° angle to the impression, then the true size cannot be produced in order to compare to the actual shoe.

Whenever possible, impression evidence is collected as is and submitted to the laboratory for examination. For shoeprints and tire tracks that cannot be picked up, various lifting techniques are used to recover the evidence. These include:

• Adhesive lifter - a heavy coating of adhesive lifts the imprint from smooth, non-delicate surfaces such as tile or hardwood floors, metal counters, etc. It is usually used in conjunction with fingerprint powders.

• Gelatin lifter - a sheet of rubber with a low-adhesive gelatin layer on one side that can lift prints from almost any surface, including porous, rough, curved and textured surfaces. It is less tacky and more flexible than an adhesive lifter, allowing it to pick up a dusty shoeprint on a cardboard box, for example, but not tear the surface of the box.

• Electrostatic dust-print lifting device - a tool that electrostatically charges particles within dust or light soil, which are then attracted and bonded to a lifting film. This method is best for collecting dry or dusty residue impressions on almost any surface, even the skin of a cadaver.

Any plastic, or three-dimensional, footwear or tire impressions can be collected by casting. Casting uses a powdered stone material, such as dental stone, that can be mixed with water and poured into the impression. When it dries, this method creates a three-dimensional model of the impression.

Imprints and impressions may be further processed to enhance or bring out additional minute details. For example, a digital enhancement program such as Adobe Photoshop® can be used to improve the quality of a photographed tire track. Fingerprint powders and chemical stains or dyes can enhance image color or increase the contrast against the background. This enables lifted or casted evidence to be photographed or scanned.

A faint bloody shoe print on linoleum is enhanced by treatment with a chemical, BLUESTAR®, to allow a more detailed photograph to be taken of the evidence. (Courtesy of Erik Savicke, Boston PD)

Comparison samples are usually taken from suspects or suspect vehicles. Shoe samples should be packaged to avoid cross-contamination and tire samples should remain on the vehicle.

A reference print from a tire is captured by inking the tire and driving over paper. (Courtesy of John Black, Ron Smith & Associates)

Learn more about tire exemplar collection ▸

Who Conducts the Analysis

Evaluation and comparison of impression evidence should be performed by a well-trained footwear and tire track examiner. Typically these professionals have received extensive training on footwear and tire manufacturing, evidence detection, recovery, handling and examination procedures, laboratory and photography equipment and procedures, courtroom testimony and legal issues, and casework.

The Scientific Working Group on Shoeprint and Tire Tread Evidence (SWGTREAD) has a published standard that discusses the minimum qualifications and training for footwear/tire track examiners. Additionally, the International Association for Identification (IAI) offers a recommended course of study for footwear and tire track examiners that takes participants through more than 550 hours of training. The IAI also certifies footwear (but not tire track) examiners.

How and Where the Analysis is Performed

Detection, documentation, photography, and collection of imprints and impressions occur in relation to crime scenes of many types. Analysis of impression evidence is typically performed at a public crime laboratory or private laboratory by experienced examiners.

Evidence Submission and Examination

Ideally, the suspect’s shoes and/or tires are submitted to the lab along with the collected evidence. Examiners will use the submitted shoes and/or tires to make test standards, impressions of a known source, which can then be compared to the collected evidence. This is usually done using transparency overlays or side-by-side comparisons.

For example, in a case from Florida, a bloody shoe print was found on the carpet in the home of a murder victim. The print indicated that there was a hole in the shoe that left the print. Investigators collected and made test prints of the shoes from individuals known to be at the scene near the time of the murder. Footwear examiners were able to identify the perpetrator by overlaying the bloody shoeprint from the crime scene with the test print made from the suspect’s shoe.

In some cases, an investigator may be asked to submit shoes or tires of other individuals for exclusion purposes, such as from a cohabitant of a home or from a first responder to a crime scene.

Tools and Techniques

During the examination and comparison, examiners use tools such as dividers, calipers, special lighting and low magnification. Examiners measure the various elements within the tread design as well as the length and width of the impressions, and then compare those measurements to what is seen in the crime scene print or impressions. Low magnification and special lighting are sometimes used to determine if various characteristics are accidental or something that was created during the manufacturing process.

Examiners perform side-by-side comparisons by placing the known shoe or tire alongside the crime scene print so that corresponding areas can be examined. Test prints are also compared to the crime scene print. Digital images on double or triple computer monitors can also be used during the comparison.

Resources and References

Investigators or examiners often use searchable databases containing reference files of shoe outsoles and tire treads to determine the brand/model of a shoe or tire. The FBI, private consultants and fee-based commercial systems maintain databases with tens of thousands of prints. Often investigators can contact the manufacturer directly to obtain information and images for a specific shoe or tire.

The FBI’s Criminal Justice Information System (CJIS) maintains the Footwear and Tire Tread Files database. The SWGTREAD website contains links, resources, information and videos to assist investigators and examiners.

Some agencies use databases to store crime scene images of shoes and tires, and to search and compare crime to crime. Searching these databases does not find potential “matches” as automated fingerprint identification systems can, but returns tread design “look-alikes” for footwear and tire tread.

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Impression Evidence: Strengthening the Disciplines of Pattern and Impression Sciences Through Research

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Forensic examinations involving specific forensic science disciplines are typically dependent upon qualitative analyses and expert interpretation of observed patterns based on a scientific foundation, rather than quantitative results. These disciplines include latent fingerprints, questioned documents, footwear, and other forms of impression and pattern evidence. This NIJ Conference Panel will highlight current fundamental research needs in the areas of impression evidence examination and how NIJ is addressing those needs through its forensic research and development portfolio within the Office of Investigative and Forensic Sciences.

Gerry LaPorte:  First of all, it's my pleasure to be moderating this panel. We have three highly esteemed experts in various fields of the … as what I'm … the terminology that I'm starting to hear quite a bit now is the pattern and impression evidence area.

So our first speaker today is Dr. Busey. Tom Busey is a professor of cognitive science at Indiana University in Bloomington. He has addressed the psychological aspects of latent print identification for the past five years with support from NIJ. Much of his current work uses eye-tracking methodologies to determine the features that experts use when individualizing or excluding latent prints. Dr. Busey received his doctorate from the University of Washington.

Tom Busey:  Thank you, and I guess this is your last of your fourth session today, somewhat of a relief.

So, despite the fact that I live in a fly over state in Indiana here, we actually have a fairly strong quantitative group and a very strong school of informatics in computer science, and my colleague, Chen Yu, is a computer scientist, and we work with John Vanderkolk, who is with the Indiana State Police up in Fort Wayne.

And even though I'm in the Department of Psychology, I like to tell people I'm not the kind of psychologist who cares how you feel. I mean, if you tell me how you feel, I'll look interested, but, deep inside, I'm not caring. Instead, I'm the kind of —

[Laughter.]

Busey:  I'm the kind of psychologist who studies how experts go through and match what can be very partial and degraded latent prints to inked prints collected from a known source. Now, this is a very difficult problem, and computers have made a go at this, but despite what you see on CSI, computers mainly play a role in triage, in finding candidate matches. And almost all of the evidence that's presented in court is done on the basis of a human expert comparing these two and rendering their opinion.

Now, there's good reason for this expertise, this superiority of the human visual system. In fact, you've probably proven this expertise yourself over machines many times through these CAPTCHAs. You're familiar with these where you have to fill them out to prove that you're actually not a computer, and, in fact, this was the basis for a recent comic that uses robots as their chief characters. One of the robots has a CAPTCHA for a tattoo, which, of course, he can't read. So you guys are in on the joke because you're not a computer. You can go through and read this and figure out what it says, and even though this is something that computers are continually getting better at — in fact, my colleague, Hari Srihari, here could probably develop algorithms that would read these kinds of CAPTCHAs.

By and large, humans have had a remarkable ability to succeed where computers have failed up until now, and the idea behind my research is to try to understand how the human visual system works, understand its elements of expertise, and then try to apply them to machine systems.

So human experts outperform kermit automated systems in many domains, not just in fingerprints, but there are lots of advantages to machines. They have known algorithms, except in the case of AFIS, where they're apparently still proprietary. They provide for standardization. They can be used to provide probabilities of random correspondence, and, of course, they can be incredibly fast. And so these are things, these are advantages that can be used. Once you develop expertise from humans and try to understand it, you can bring those into the machine world.

So, to do this, we're proposing to reverse-engineer the human visual system, study it and try to glean its secrets, and then apply those using the kind of language that machines understand.

Well, this is not inconceivable because, in some sense, the human brain can be thought of as a computing device. In fact, your retina, the back of your eye that actually does the light capture and the first steps of seeing, is actually made of the same stuff that your brain is. It's a little piece of brain tissue that was segregated out early on in development and brought out as part of your eye. So your eye itself is a little computing machine.

So we want to know what makes your human visual system and your eyes so good. Well, one point, one starting point might just say, “What do you use, experts; how do you go about this task?” It turns out to be a good starting point, but language is a very poor representation of what can be a very rich perceptual experience.

So imagine doing a latent-ink comparison over the phone here. So you have this print right here. Your colleague on the other end of the telephone has this print. Imagine trying to describe this in such a way that they can decide whether this matches or not. It's a very difficult process to imagine doing, and it illustrates how poor language is as a means of describing perceptual information, at least complex perceptual information here.

Another problem is that some processes to perception simply reside below the level of consciousness. We can't change some of our perceptions, even though we know that reality differs from our perceptions.

So this is a very famous illusion here. I don't know if it's going to work at this distance. What you may find, if you stare in the middle here, is that these outer ones start moving on you. It can be very disquieting to see this, and you can't not see it happen. You can't tell yourself just because you know it's static that the motion isn't there.

So, usually, our percepts are trustworthy. There are some situations where they're not, which is another reason to study human performance.

In addition, experts sometimes report having an “a-ha” moment where they just look at these two prints and they know they match, and then they have to go through and document it, of course, but the process of this immediate spark of recognition is way too fast to come from a serial, sort of point-by-point comparison.

So, in order to understand how experts are doing this, we are using eye-tracking technology. Here is an example of our eye tracker here. There is one camera on the front here, which looks at the eye, and there's another camera up here on the top of the glasses that looks out on the scene. And we end up with two images right here. You have an image of the eye, and we can capture the pupil here and what's known as the “cornea reflection,” and then we have another camera that tells us where the scene is relative to the head, and there's a very straightforward calibration procedure that allows us to look and see where the eye is relative to the world. So we can see what people are looking at.

We can estimate the pupil and the cornea reflection with our software, which tells us where the eye is gazing, and all of this is wrapped up in a software package that's free and open source called Expert Eyes that allows you to go through and collect data and analyze it. And we have data for over a hundred subjects now that we've analyzed, and I'll talk about a subset of that data today.

Now, we want controlled studies that allow experts to reveal those regions that they consider most diagnostic, and, therefore, we fix the viewing duration of each pair of our prints to about 20 seconds to give the experts just enough time to tell us what they consider to be the most relevant or the most diagnostic.

So here's an example movie of our eye tracker at work. Here's the eye moving up here, and here are the crosses here as our estimate of where they're looking in this gaze. When it jumps there, that's the blink, and that's, of course, not relevant data for us. So you can really get a sense of what features they're relying on when they're going through and doing this particular task. So that was one trial worth of data.

Here's another visualization here. The blue is a trace of the overall eye data within the trial, and then the red plus signs that are filling in, those are the actual moment-by-moment fixations. It's kind of actually hard to watch that in real time because you're essentially watching somebody else's eyes, and it's tough to keep up with other people's eyes.

So here's another visualization here. The green circles are the fixations, and then the red is the current fixation. And the size of that tells you how long they spend at that location. Even with this visualization, I had to slow it down to half speed to enable you to keep up. So this gives you an idea of how they're going through each location and determining what features they think are most relevant.

So our system is fairly accurate. The felvia [ph] is about the size of two thumbnails' width at arm's length. So, if you think about how wide your thumbnails are, that's about where you're acquiring information. That's the region of fairly high acuity of the visual system. The resolution falls off rapidly after that, out in the periphery.

And our eye-tracking system can resolve eye gaze locations down to a half a thumbnail or about .5 degrees of visual angle. So we're down to the point where we're in the region and below the region of where people are acquiring their information.

And this, in general, from most of our experiments, corresponds to about one to four ridge widths, depending on the size of the ridges that we display.

Now, the first step here, if you're going to try to take data from experts and use it to improve machine systems, is first verify that your experts have some kind of expertise. There hasn't been a lot of work done in eye-tracking work with experts, and so this was our sort of first step to make sure these experts really do have this special expertise.

So we did our first experiment with 12 experts and 12 novices. Each one did about 35 pairs of images, and it's useful to talk about the behavioral accuracy first because we want to make sure that they're actually doing better than our novices. What we find is that we have three categories of responses. So they can either say “yes, there's a match”; “no, it's not a match”; or we gave them an option of “too soon to tell.” We found that when you pushed our experts after only 20 seconds to make a decision, they really were uncomfortable with that because for an expert, if you make a wrong decision, it's a career-ending move.

So we added this “too soon to tell” option, and we found that many of our experts are fairly conservative. They're not likely after just 20 seconds to move into this “yes” category here. So a lot of them are saying “too soon to tell.” They make very few errors when it comes to misses relative to our novices, and most importantly, they make no erroneous identifications. None of our 12 subjects on any of our 35 images made erroneous identifications; whereas the novices, 25 percent of the time, it was a true nonmatch. It should have been an exclusion. They're saying, “yes, it's a match.” This is a huge difference, and that really drives the performance accuracy difference between the two groups. So the experts are doing much better than the novices.

The second question we asked is whether experts as a group are consistent with each other. We'd like to think that there's some kind of implicit standard that the experts are relying on, a set of features that they all agree that are relevant for the task. We could ask do they tend to visit the same regions or locations.

And I'm going to quantify that just briefly for you by imagining that your eyes shoot out laser beams, and when you're kids, I'm sure you imagine this. You're shooting out laser beams, and where the gaze hits, the print turns dark red here, yellow and then red. So this is a way of visualizing where the eye gaze falls, and we can compare the patches from one expert to another expert and one novice to another novice to ask are the experts more consistent as a group or are the novices more consistent as a group.

And it turns out that the experts as a group tend to be much more consistent in terms of the regions that they visit, and at least in this constrained viewing experiment, experts seem to have an implicit set of features that they tend to seek out and tend to all look at.

Here's an example of why this is happening. This is kind of a dim slide from this projector, but most of the experts in green here are falling in this region here and also in the analogous region on this side, whereas the novices in red are all over the place.

Here's another way of viewing what features the experts are relying on. We can use automated software to identify the locations of minutia, which are marked in green pluses here, and if we look at regions surrounding those of some arbitrary circle size and ask within each fixation how many of these minutia fall inside of it, do the experts tend to look at more minutia than novices? A lot of the AFIS systems are relying primarily on the locations of minutia. So you could ask, “When experts are doing these latent-ink comparisons, do they rely on minutia?”

Well, it turns out that when you count the number of minutia inside a circle of fixed size centered on each fixation — and the size of the circle doesn't matter, it turns out — and ask whether experts or novices have more nearby minutia, it turns out in these latent prints, there's no difference between the experts and novices in terms of the number of minutia that they visit. And this suggests that for latent inked comparisons, the minutia may represent a relatively small part of the available information. Experts might be relying on ridge flow or curvature or level three detail or something else when they have a relatively small patch of latent impressions.

So this highlights the advantages that humans have over existing computer models. They have the ability when they don't have a lot of minutia to move to other levels of detail, and to do that fairly easily and quickly; that's something that the computer system is going to have to — if it can process something like level three detail — it has to decide whether it should represent that or minutia detail in its decision and how to weight those.

OK. What about for clean prints? Well, we used a second set of clean prints, and we found that experts now are much more likely to move their eyes to locations closer to minutia. So, if you give them a large wealth of information, they're likely to move their eyes more to minutia.

And here it's obvious why. With these clean prints, the experts tend to move their eyes to regions near the core, leading from the core and also the delta. So the experts are in red here, the novices are in blue and the minutia are in green. So the novices tend to be sort of all over the place, and the experts tend to be down in these regions here.

So a third analysis that we did was to look to see whether experts move their eyes more quickly to matching locations. So we asked one of our experts to simply place locations of correspondence on these two prints, and that's what's shown connected by lines here. And then we can imagine that there is a trace starting right here and ending in a fixation right here, and it's the last fixation before they move with their eyes to the other side, so when the eyes moved over here looking for a matching corresponding location, which happens to be this green dot right here, and then they sort of wander off, having found it as close as they're going to get.

So we can ask how close do subjects get to this matching location or over time how close are they getting at each point in time to this matching location.

Well, it turns out that this graph here illustrates the time since they moved their eyes from the left side to the right side. That's time zero. And the Y axis here is the distance from what is the true matching location, as determined by where they started on the left-hand side, what's the right-hand side matching location, and we find that the experts almost immediately get closer and stay closer to the matching location. Ultimately, they are much closer to the matching location than the novices are.

OK. So experts are better than novices. They are more consistent as a group in terms of the features selected, and they have a tendency to rely on more minutia when looked at inked prints.

But the final question I want to address in the last few minutes is what features they rely on, because minutia may be only part of the story. At least for latent prints, it doesn't look like experts are using it more than novices.

So this is a more complicated analysis here, but basically what we do is we start with the fixations, which are shown in red here, and we'll crop out regions of pixels surrounding each fixation. So here's a little crop of pixels. Here's another little crop of pixels. We can get lots of these. In fact, we get 40,000 total over all of our database here, and then we can construct … do what's called “dimensionality reduction,” which basically illustrates the fundamental building blocks of perception. These are sort of the alphabet in perceptual language of fingerprints, and the amazing thing is that you can take relatively few basis functions here and reconstruct with fairly high accuracy the original image patches.

So here's an original image patch here, and here is the reconstruction of that here. It's a little bit blurry, but it's remarkable, the fact that you can take 40,000 image patches and with just 200 basis sets reconstruct any one with fairly high accuracy. It's actually not that unfamiliar or unrelated to something like JPEG compression, where you can see enormous redundancy reductions.

So what we'd like to do with this analysis is to figure out where … what features or combinations of these basis sets are used by experts as they go through and look for correspondences between prints, and this is really the heart of our grant project over the next couple years is to identify what combinations of these features are used by experts.

You can see that they're already starting to see some complex features like minutia coming out of these, but you also see lots of categories where it's just ridge flow, and so, by looking at the combinations of these features, we can really see which of these distinguished between experts and novices and ultimately — and I'm going to skip all of this in the interest of time — ultimately try to figure out what our features will look like that experts are using. And this is really going to lead to a situation where you can essentially look through the eyes of the experts and use this to filter images, so that we can highlight regions in new prints that we think experts might find most diagnostic.

So implications for practice here, just to finish up: machine learning results are directly applicable to computer-based systems like AFIS because we use this same language that they're using. We use machine learning approaches, which are the same kinds of tools that are used in systems like AFIS.

Prints filtered by this basis set that I just showed you can assist the identification process, much like we have computer-enhanced mammography examinations now in hospitals. The feature set that I just showed you provides a quantification of factors, such as feature rarity, something I think Hari will talk about as well, and it can be combined with likelihood models to provide probabilistic statements, much like we have in DNA evidence now.

But, first, we're going to need to collect the relevant data from experts and infer appropriate feature set, and that's really what we're doing in this project.

[Applause.]

Gerry LaPorte:  Our next speaker is Dr. Lynn Abbott, and Dr. Abbott is an associate professor at the Bradley Department of Electrical and Computer Engineering at Virginia Polytechnic University, a.k.a. Virginia Tech. Dr. Abbott has more than 20 years of experience in the area of image analysis, and he's the co-principal investigator on an NIJ-funded project titled “Establishing the Quantitative Basis for Sufficiency Thresholds and Metrics for Friction Ridge Pattern Detail Quality and the Foundation for a Standard.” This was a grant that was … it's a 2009 grant, so they're not quite … I think we just got everything, all the logistics of the grant, completed probably this past February or March, so we're early into the process.

Lynn Abbott:  Thank you very much. Yes, primarily I'll be describing a project that we just got under way. We had our first meeting with students last January. So we're a few months under way, and so my plan is to talk about our approach, some of the philosophy we have behind our approach.

And it was very interesting for me to hear the work of Tom a few minutes ago because he talked about human visualization. I come from what we call “machine vision,” computer vision, much of which is motivated by this wonderful existence proof, which is what human visual systems can do, biological vision systems in general, and some things machine vision systems can do much better. They're much more patient, for example, but there are many mysteries left in terms of what human and other biological systems are capable of doing.

And so the high-level objective of our project is to establish some sort of quantitative basis that can be used to develop some sort of sufficiency analysis for fingerprint image quality, friction ridge image quality. The motivation, well, the Daubert case provided the fundamental motivation, but, in essence, what we are trying to do long term is provide some sort of scientific measure of confidence in an examiner's decision with an emphasis on latent prints because, after all, this is an NIJ-funded project. We are interested in and motivated by many of the AFIS work, but our goal on this particular project is to somehow come up with a way to provide that confidence measure, somewhat semiautomatically but not necessarily completely automatically.

A little bit about the project team: We consider our ringleader to be the fellow in the lower left, Randy Murch, a fellow who's named and is also on the left of that picture. In that photograph, he was demonstrating to us how we could go about collecting some prints for use in our own experiments, and working with him are a couple of students and Ed Fox, who is a professor of computer science. Also in our project, Michael Hsiao, who is a well-known expert in the area of digital circuit design and testing.

Let's see. Bruce Budowle is here, but he's not in this room. He's manning a station outside with a poster presentation that he has. Both Bruce and Randy have several decades of experience each in the FBI world, and so they represent … they bring to our project a lot of expertise on the law enforcement side. The rest of us are really computer guys who are learning from them but bring, hopefully, some expertise into image analysis and matching and so forth.

When I sat down to make up the talk, I said, “Why don't I compare what I know about the AFIS world, Automatic Fingerprint Identification Systems, versus the latent print world?” When we talk about so-called “clean prints,” often called “plain-to-rolled” or 10 print images, they often are high quality, at least relatively high quality compared to many of the latent cases. And the reason is latent prints are inadvertent almost by definition. So they tend to be blurred or partial, and there's been not very much works to the extent we can determine in analyzing such poor-quality images, at least from a point of view of analysis of fingerprint quality.

The AFIS systems, to the extent we can learn about them, emphasize minutia, ridge endings, bifurcations; whereas the experts, such as places like SWG, SWGFAST and other sources like that, constantly emphasis multiple levels, especially level one and eventually level three as well.

Also, many of the commercial systems, we're told — they don't tell us details — typically use binarized images. So every pixel is either on or off, as shown by the middle image; whereas we would much prefer in my world to work with grayscale images. Each pixel is at least eight bits or so, typically, of information.

One thing also in discussing, in our discussion with experts, are the level two features alone are insufficient, and here is a contrived example which we are told mimics reality. It's possible to find a lot of level two details which match perfectly, but when you fill in the level one details, the ridges, there's an obvious mismatch in some cases, and so we've been told there are examples like this. And so reliance on level two alone is not good if we're talking about quality in some sort of courtroom environment.

And so one thing we have done in the last few months is to investigate what can be done using level two minutia, and so we are not trying to reinvent the wheel here. So we very happily adopted the NIST software, the NBIS package, which contains this procedure called MINDTCT or minutia detection, and you see the results of that on the left, if you can make them out. They're little colored squares indicating where the minutia points sit.

And then, because we are interested in grayscale approaches, we found this paper by Maio and Maltoni, which talks about analyzing the actual ridges from a grayscale perspective in an effort to find the minutia points.

One of our students implemented this work and came up with the dots that you see there. We ignore those false matches on the far left because there's extra work needed to identify the edges of the useful image.

If we zoom in to these yellow windows and take a very careful look, there are, in fact, quite a few small discrepancies, and I don't know if my pointer works here, but here is one that sort of stands out at me. This is from the NIST software. Supposedly, it's a ridge ending. It's out in the middle of nowhere. It's between two ridges, and that's more common than you might think.

There's another case … well, in the interest of … our grayscale method did find a much better localization in that.

Also, there's a lot of discussion in our group concerning what are called “lakes.” This is a lake area here. It's between two closely spaced minutia bifurcations, and if you carefully try to take the placement there, there might be some … you might argue that there's some better placement possible. We think our placement is a little better here. So there certainly are improvements that could be made, and no doubt, the commercial world has made such improvements, but they won't give us their details.

Now, here is another experiment. We sent one of our students on to an interesting experiment where we said, “Take this NIST database that's called Special Database 27,” and we're very happy to have it. It contains ground truth, as indicated by several human examiners, for approximately two or three hundred cases, and so we picked one particular case.

The human examiner, according to the database, identified 16 minutia in the left image, 98 in the right, and from the 16 on the left came up … was able to make … identify 14 matches between those two images.

So we said to ourselves, “Well, what could the automatic software do?” We simply provided the left image. As messy as it is, we provided it to … sent it into the minutia detection software, and it popped out with what it estimated as 500 or so minutia. The clean image on the right, it came up with about half that number, and in this 500 on the left, this included the 14 found by the human examiner, so we said, “Well, let's see how good those particular 14 matches seem to be, and how can we do that quantitatively?”

Well, the idea that we got after looking much more closely than we ever especially wanted to at the NIST software, we found step five, which is called “remove false minutia.” Then we asked ourself, “Well, what would cause the NIST software to remove some minutia?” and we found that in order to make its decision, it employs nine different independent software filters, subroutines, and based on the results of those subroutines, it decides whether or not to delete one of those minutia points from further consideration.

So we said to ourselves, “If it were ever to decide to remove a point, well, it must not have been a very good point to begin with, and so why don't we investigate that a little further?” Well, the student liked the idea of a straightforward task, and so the task we gave him was to consider every possible combination of those nine independent filters. If you do the math to the power 9, 512 different on/off settings for these different minutia; luckily, he's good at automating these things. He wrote a quick script, set the software running for a few days, and the underlying idea was that higher quality minutia ought to survive processing even under all these different cases of filter combinations.

And these 14 matched points, as determined by the human examiner, were assessed based on whether those particular 14 points survived all these different filter on/off conditions, and it turns out that eight of those 14 were not modified in any way by the processing, even though the NIST NBIS score associated with those points was low, and so that's the top row on that, the table at the bottom.

So, just our intuitive idea of how these points should behave in the presence of different filtering in this case would say that we should give these points high scores, these particular eight points high scores, whereas, in one case, the NBIS said no, it's a low score based on image contrast and proximity of other points and so forth. And so, at least in a couple of cases, we found where our initial thoughts differed from what the NBIS automatic score produced, so that was one study.

Partly based on these ideas and also based on our interviews with some human examiners, we wanted to study ridge detection more thoroughly, and so we went back to that paper by Maio and Maltoni. And, as I said, my student implemented that approach and detected all the colorful ridges that you see on the right from the software he implemented. So taking the image on the left, he can automatically detect the ridges on the right, and if we want, we can overlay them to see how well they match the original image.

And based on that, he has developed some early results. He's implemented a matching approach based on complete ridges, and so this is fairly hot off the press. Just this week — well, late last week — he said, “Here's what I got so far.” So, out of 20 ridges … out of 26 ridges he automatically detected in the left image, his software fairly quickly detected 20 correct matches on the right image, and this is using a fairly straightforward cross-correlation approach. Because of time, I won't go into great detail on that.

Another area of interest deals with blurs, smears — you can use other names for this — and we're intrigued by the fact that some of this can be automated. We can synthetically do some blurring, and as soon as we can do it synthetically, we can generate lots of test cases and see how well some of the standard matching techniques will work.

So here, actually, is an ink print created by one of us just to show the direction we want to go in. So you can see progressively worse blurring from right to left in this case.

Synthetically, the same kind of blurring is fairly easy to generate, and we can argue later on about how closely this blurring matches the physical ink blurring, but with a fairly simple linear filter, we can do 5 , 10 , 15 pixel blurring on the right. And this is as if we've just pulled the finger downward on the page. That's all it's supposed to resemble.

And based on this, one of our students took the blurred images, fed them into the NIST software to see how well does it produce … how well does it detect these minutia points and then how well are matches performed. And in this particular study, in this one case, he found that up to, oh, about seven-pixel blurs or so, we were still getting very good matches, and so we found that to be a promising step. With some small amount of blur, we were getting just as good results as if there was no blur performed.

And that led us into other discussions of just what could we do not just with blur but with partial prints. What you see here is an upside-down plastic serving tray from Kmart or Walmart or somewhere, and it's got lots of ridges on it that lead to missing points, missing areas in the ink prints you see on the right. And so one thing we are debating among ourselves now is just what could we do to handle cases like this where there's lots of … where the prints are only partial in a very dramatic way.

And just the initial idea we have so far is to subdivide the image. Here, we happen to show a three-by-three subdivision of the image and as shown on the left, and then you see some of those subdivisions join together going on the right. And the idea is to see, well, how many of these need to be joined in able to get a reasonable number of points that can be matched.

And so there's some graphs here I'll skip because Gerry is tugging at my elbow, and so let me lead.

We were also doing some work modeling the shape of the finger, some database work.

Let me close with a couple of observations. We thrive on databases. To the extent people can provide or organizations can provide databases with some sort of ground truth, that is absolutely invaluable to this kind of work, and so we are very happy to have the NIST database SD27 available for purchase to us. So we've been making heavy use of that.

On the other hand, vendors have seemed to have been fairly reluctant to give us any information at all. We decided we'd like to buy a live scan system. So I spent 10 minutes on Google to find out, well, what are some of the major vendors. I called them up — well, I had my student; I tried to delegate — had him call them up, and one person said, “Well, just send me your questions in e mail, and I'll get back to you.” Well, we're still waiting for her to get back to me, even after I sent e mail. And a vice president of the company called me and said, “Well, we'll give you that information really soon,” which we're still waiting for that.

The other person we got did give us information, but I can see why he was a little hesitant because there are all kinds of unusual charges. They wanted to charge us $500 just to install software on our laptop, and even though I could … if I buy a very complicated software system from anywhere else, I could click the mouse a couple of times, and it installs somehow. I don't know why they need to do this.

They claim that one of the main reasons that they need to install the software is that there are so many different standards among different enforcement agencies, and so he said, “Well, there's 20 different standards.” I'm not sure what the number is, but he gave as his example, agency number one wants the name as John, space, Doe; the other person wants the name as Doe, comma, John. And all these slight inconsistencies among the way data are reported gives them a great excuse to charge more for software. That's the way I see it.

And so there's a bunch of references here that I relied on in doing work so far. I'll be happy to talk more offline.

Gerry LaPorte:  Our next speaker is Dr. Sargur Srihari, and he is a distinguished professor in the Department of Computer Science and Engineering at the University of Buffalo, The State University of New York. Dr. Srihari is the founding director of the Center of Excellence for Document Analysis and Recognition. His work led to the first automated system for reading handwritten postal addresses in the world. An author of more than 300 papers, three books and seven U.S. patents, he served on the Committee on Identifying the Needs of the Forensic Science Community with the National Academy of Sciences. So he's one of those NAS members.

Sargur Srihari:  Yeah. I was one of the quiet guys there.

LaPorte:  Dr. Srihari is a fellow of the Institute of Electrical and Electronics Engineers and of the International Association for Pattern Recognition. And, unfortunately, me being a University of Michigan fan, Dr. Srihari is a distinguished alumnus of the Ohio State — I'm sorry — “The” Ohio State University College of Engineering.

Srihari:  Thank you, Gerry. I hate to sit down and talk, but I guess I'll try it.

So I will try and speak on different types of impression evidence than the previous talks on fingerprints. Of course, fingerprints, I think, probably are the most important of the impression evidences, but there are also these other areas, handwriting and footwear evidence, and I'll speak about the kind of research we are doing, particularly for the characterization of uncertainty. And, specifically, I'll talk about how does one go about characterizing the rarity, which is the kind of thing that the DNA evidence does. You know, people are able to say that the chance that this is matched is one in such a large number that it's got to be this person, that kind of thing, so can this be done with things like handwriting and with footwear, and so where do we begin?

My first slide here is just forensic modalities. We talk about impression evidence. I think for the forensic community, this is all quite well known. You know, this is just a tree describing impression evidence. It includes latent prints or the kind of forensic evidence we saw earlier on. It also includes handwriting and question documents, printers, footwear, tire tread, firearms, tool marks, et cetera. So many of these shows of characterizing uncertainty, I think, is common to all of these fields. But my talk is going to be largely about the handwriting and the shoeprint area now.

Now, why do we need uncertainty models at all? Why do we need to say how sure are we? Courts have long allowed testimony of certain individualization or exclusion or inconclusive. So it's possible for testimony to say that this is individualized; this is this person and no other or exclusion as well. But, of course, as we have seen over the last many years, several exonerations have been taken place and misidentifications and so on, so that it caused a lot of concern, and among other things, that also led to the formation of the NAS committee, and the NAS committee recommendations include a study of these kinds of issues. So those are some of the background as to why academics should do some research on this issue of how to characterize uncertainty.

On the other hand, one can say there's nothing new about uncertainty. We heard today the lunch speaker talked about Benjamin Franklin. Here is another quote of Benjamin Franklin, was that nothing is certain but death and taxes. All right. So, also, we can say in forensic testimony, also, it is not certain; nothing is certain about individualized exclusion. There's got to be some level of uncertainty about it, and it's better to be able to say that in some quantitative manner.

So this issue of expressing uncertainty itself has a lot of some uncertainty in it. How do you go about doing it? One can take measurements from evidence of the kind we talked about in fingerprints. I'll now talk after this slide about handwriting. One can take the measurements from that, and one can compute from that something called “rarity,” which is a joint probability of those exact features.

Let me see if I can use this. Yeah, rarity.

So this will be based on … you make some measurements as how unusual is this thing, how unusual is this piece of handwriting or how unusual is this particular structure of the fingerprint. So you can call that as a rarity.

Another thing is when you have a known and the evidence, one can compare the two together and say how similar are they, how sure do you think these two are one in the same, came from the same source, and we can call that as “similarity uncertainty.”

Of course, one can say, “Well, you know, this could also be combined together and expressed as one level of uncertainty,” but there really are two different things here, the rarity of the basic structure itself and then what is the uncertainty in the comparison. So there are probability models for each of these things.

And similarity usually depends on a particular similarity measure. How do you measure the two things are similar? What is the distance, in Euclidean space or whatever space? What is your measurement of similarity? With respect to the NIST software that talked about earlier on, that is what is called a [inaudible] measure. So it gives you a particular score that says how similar are these two things.

And, of course, once you get into similarity, one can talk about likelihood ratios coming under the hypothesis that they are from the same probability distribution of what is the similarity score; how is it distributed when the two came from the same or when the two came from different sources; what is the distribution; and one can use those two probability distributions to compute what's called a “likelihood ratio” and express that as a ratio of the prosecution hypothesis to the defense hypothesis and say that is the strength of the conviction here. Of course, one can model it in many statistical ways using what are called less-frequent Bayesian models and so on. All right.

So let me now get into how these kinds of ideas can be useful in an area such as handwriting, in handwriting comparison. Here, we have the classic handwriting comparison case. We have a known piece of handwriting. It comes from a database we created of handwriting samples, and then we also have here a question, a piece of handwriting. All right.

So, when comparing these two, we have letter shapes. We have shapes of piece of letters we call as “bigrams”. We have shapes of words and things like that. One could compare all these things to see how similar is the writing. One could also look at what are called as “macro features” or pictorial features looking at the spacing between the lines and the words and so on. So lots and lots of things a question document examiner uses to determine whether these two samples were written by the same person or not.

Well, we took on this issue. Ideas of expressing uncertainty in handwriting have been around, and the question document examiners have thought about this, but they really did not have the computational tools available. It's a massive task to be able to compute all the underlying probabilities to be able to say what is the probability of these two structures being one and the same.

So we first have to begin with what are the features of handwriting. So here is an example of a “bigram,” I call it. It's the letter pair "t h". So document examiners have thought about this — how do you describe this "t h"? So here is a table on the right-hand side.

So, you know, how do they characterize the shape of a t h? Height relationship between t to h, is t shorter, t is even, t is taller or no set pattern. Shape of loop of h, is this loop of h retraced staff, curved right, straight left, curved left, straight right, both sides curved, no fixed pattern. Shape of arch of h, I guess it's this one here, rounded arch, pointed arch, no set pattern. Height of cross on t is in upper half, in lower half, cross about, no fixed pattern. Baseline of h is slanting upward, slanting downward, even, no set pattern. Shape of t, single stroke, looped, closed, mixture of shapes. This is the kind of thing a document examiner would look at when he looks at this t, he or she looks at this "t h" and saying that is the basic description.

And we chose here "t h" because "t h" happens to be the most commonly occurring pair of letters in the English language. Second most is "e r". Third is "o n," et cetera, et cetera. So "t h" is the most common one that occurs in a lot of writing, so we said let's look at t h. So it's necessary to compute these kinds of things.

It's very complicated to compute these things by just looking at this image. So we computed a bunch of things here, and we said, “Well, we can compute four different things: height relationship of t to h, presence of loops, t and h connected, slant of t and h.” And then we said, “Well, 47 percent of the time, the t is shorter; 22 percent of the time, t is even with h; and 29 percent of the time, t is taller than h.” Loops, 10 percent loop only in t, 11 percent of the time loop only in h, 6 percent of the time loop in both, 71 percent of the time no loops like that. This is the kind of thing.

How do you do this? By looking at a world of t h's extracted from a lot of writing and say, “How often do these things happen?” So, once you get that, you can calculate what's called as a joint probability. So you observe this particular t h, which consists of the four features, X1, X2, X3, X4. X1 could be height relationship; second, the presence of loops, et cetera.

Now, once this is called as a joint probability and probability theory, once you have a joint probability that can be expressed as a product here, which consists of what are called as “conditional problem,” X1 given X2, X3, X4; X2 given X3, X4; X3 given X4; et cetera. It's pretty … you know, a long expression like that which is called a “joint probability.” So that's what needs to be computed.

Now, computing something like that is not easy. You're going to have to have lots of examples. Even for t h, you know, there are thousands of exemplars. Every one of them has to be analyzed, and you compute them. And then you need to express these kinds of things. It gets too complicated, and so, although the idea has been around, nobody has been able to compute these things before.

So, well, one can look at what are called as “independences.” This is basic probability theory. Are two features independent? If two features are — if X1, X2, X3, X4 are all independent, that probability calculation is very simple. Simply multiply all the four probabilities, and you have it. But that would be a very inaccurate calculation because they are dependent on each other, and you've got to determine which of these features are dependent on others. So we have to figure out all these conditional independences, and if you figure out that some of them are conditionally independent, you get a simple expression. So one could possibly compute that.

OK. So idea here would be we encounter a particular t h in a handwriting, and the forensic examiner can testify saying, “That is an extremely rare t h, and that is the particular probability that it occurs at all,” so one can make such a statement analogous to, say, DNA and so on.

So we went further here, condition probability tables. We got to compute all these things, and so here, there are … one is a height relationship between t h, presence of loops, presence of loops given the other variables and so on, so an extremely large number of tables have to be computed in order to have all those conditional probabilities to be able to figure out the final probability.

So, actually, we did this. We did this for the letter t h. We went about looking through 1,500 people's handwriting and extracted t h's, computed underlying probabilities of all of these things that a document examiner wishes that they knew, so that they could calculate it. That way, we are able to say here are several t h's.

So this particular t h here, as I said, probability point, zero, zero, zero, four, something like that. This particular one has t is taller than h. Neither have a loop, and t and h are connected, and slant of both t and h is positive. This one is shorter than h and so on. So we get different types of probability for each of these things.

So the starting point here, you can say, “How unusual is this particular t h?” And, of course, if we have much more handwriting available, we can now look at all of them together and say, “What is the particular probability associated with that?” So this is a style of calculation is called “rarity,” evaluating the rarity of this type of handwriting.

Now, to do this is an enormous task. You got to be able to take thousands of samples of t h, and that's not the only combination in the English language. You're going to have all the letters of the alphabet, all combinations and so on. So you'll have to look at all of these things. So it's infeasible to do it manually. So we've been developing computer programs to do these kinds of things. First of all, when you have handwriting samples, how do you associate the truth with it, are we looking at a t h here or an e r here and so on. This is called … this is an interface where you can type in the ground truth here. It matches it with the handwriting and associates the handwriting with the ground truth, so that you have all these pairs, and you can extract them all out. Here is the world of t h's now, and to that, we are to apply the feature extraction algorithms.

And the features are extremely different for everything. We saw what it was for t h. For an e r, it will be totally different set of features, so on, a very large number of such features. They all have been computed. So it's a fairly laborious computer programming task to be able to extract all these features so that you can apply it to a large database to extract the particular frequencies.

So that's about rarity computation. So the usefulness of this … it's simpler to say how unusual is something. The next one is how do you now characterize uncertainty. If I give you two letters e and then I ask you, “How similar are they and what is the strength of the opinion,” you got to capture their similarity in feature space.

We have some simple method here that we're using, and once you have the measurement and you have now two distributions that are corresponding to the similarity measure, when they came from the same, called as the “prosecution hypothesis,” and when they came from two different writers, it's called as the “defense hypothesis.” The prosecution and defense hypotheses are two probability distributions.

This is for the letter e. This is for slant in a writing, and those are two different distributions. So, once you have these distributions, one can … given any pair, saying I've got an e here, I've got an e here, what can you say about it? We can measure the similarity, and we can read off the two probabilities from these distributions and express that as a likelihood ratio.

So the computer program that actually does this kind of thing — here is a k; here is another k — and it computes not just the likelihood ratio but what's called as a “log likelihood ratio.” It takes the likelihood ratio; it takes the logarithm of it. If it's positive, then it says it sounds like it's the same writer. It's 2.145 here; it means similar case.

This k and this k … this one has a loop here. This one doesn't have a loop. This is minus 0.2894 saying these two k's probably were written by two different people. These two, more similar. These two is somewhat not similar, that kind of thing. So one can have a characterization of the likelihood ratios or the log likelihood ratios for all pairs of k's.

Now, one can accumulate all these types of information for every letter, every pairs of letters in the two documents, the known and the question, and come up with an overall log likelihood ratio. Of course, if you consider them to be independent, not independent, some subtle issues come about.

So, for example, here is one sample of handwriting. Here is another sample of handwriting, and the log likelihood ratio comes out here. That's 41.52. It's a large positive number and which gets mapped into the scale identified as same, highly probable, same. This is a nine-point scale recommended by the SWGDOC. The technical group suggests a nine-point scale for question document examiners in the U.S. It's a five-point scale in Europe and so on. So one could map these kinds of numbers into this kind of an opinion as to how sure one is about the writing.

This is again a computer program interface. Here are two handwriting samples displayed here, and these are the kinds of features that are being used, and this is another pop up screen that says these two handwriting samples are being assigned to the category highly probable, they were not the same writer for this particular case.

OK. That's about question documents, and let me just wrap up the last five minutes with footwear comparison now. It's the same kind of thing in all of impression evidence. So you have a question. The terminology is a little bit different when you go from one type of impression evidence to the next. In handwriting, we call it “question documents.” In fingerprint, we call them “latent prints.” In the case of footwear, we call them as “crime scene impressions.” Right?

This is the crime scene impression, and here are some several known prints over here. So the task again is how rare is this particular pattern, and we haven't really looked at that much at this point. We look at the similarity here, like can you compute the similarity between these two and give us some kind of a strength of the match.

So we have to do a lot of pre processing, image processing and so on. So we took on that shoeprints are manmade artifacts, objects. They're not natural things like fingerprints. They are made of geometrical primitives. We looked at lots of them. They seem to have lots of circles on them, ellipses on them, lines on them, et cetera. There seems to be all these primitives. So we said, “Let's detect all these primitives”; these become our minutia or whatever for shoeprints.

So we detect circles, ellipses and lines, and then we — now what are we after here? So, once you got all these primitives, you got to have a measure. How do you say these two prints are from same, or how similar are these two prints? So, for that, we compute what's called as an “attribute relational graph,” which represents the whole shoeprint now as a graph of some form, and we have given two graphs now. One can have a graph distance measure. So that's the kind of thing we do, and we have defined what's called a footwear print distance.

So, given two footprints, I can say how similar are these two things. Given any two piece of footwears, it's highly mathematical in nature, but, anyway, that's a little bit of formula to indicate to you. It's a complicated formula that's out.

So here, we have footprint distance values. Here is the crime scene print. Here are known prints, and we take that and say, “Well, what is the distance based on this?” For each of these, we calculate this value.

And why is this useful? You have a database. There are literally thousands of possible shoeprints in our shoeprint database. So, when you have a crime scene print, one could match against every one of them. It becomes a problem of image retrieval. So you can say, well, I find the closest match to be … let's say it's Nike here or an Air Force, you know, One or something like that. We can make it pull that up.

And we can do things like cluster all these things. That's the kind of thing we have done. We have taken a large shoeprint database of thousands of prints, and we have clustered them into groups so that very quickly we can go and say, well, this particular crime scene print seems to belong to this particular cluster, and how do you cluster things, you need a measuring similarity between prints, and that mathematical formula that I showed earlier on, there's that.

And once you have this kind of thing, one can also get into the similarity distributions within class, between class. When you have crime scene prints and the knowns associated with it, one can have distributions, and one can compute things like probabilities under the prosecution hypothesis, probability under the defense hypothesis and so on.

Here is a crime scene print, and here are the known prints. For one of these, for the closest matching print, the shoeprint distance is .0605. The probability of the prosecution hypothesis that it was done by the same is .2950. So we now have a concrete probability associated with could it be the same.

All right. Summarizing. Nothing is certain, including forensic matching. Features and similarity measures are needed for computing uncertainty. So we begin with what are features and then how do you measure similarity between two objects, and then we now map into … we work in feature space to talk about rarity, how unusual is this particular combination of features, and then we work in similarity space. We look at similarities of pairs of things in the world, and then we say, “How similar … what is the uncertainty associated by means of a likelihood ratio for similarity?”

And then, once you have a likelihood ratio like that, you can then map it into just an understandable opinion. Instead of numbers, you would simply say it is likely that it was done by the same. Right?

And, in order to do all of this, there's an enormous amount of underlying probability extraction, how often do these features occur, and in order to do that, particularly in the case of handwriting, you need computer tools to be able to analyze large quantities of handwriting, to extract the relative frequencies of all of these things. So one can automatically say, “I see a particular structure,” and then it comes out and says, “Well, the probability of that structure is this.” So that's the kind of work we're trying to do.

Thank you very much.

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Opinions or points of view expressed in these recordings represent those of the speakers and do not necessarily represent the official position or policies of the U.S. Department of Justice. Any commercial products and manufacturers discussed in these recordings are presented for informational purposes only and do not constitute product approval or endorsement by the U.S. Department of Justice.

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Impression Evidence

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Minute imperfections on a large variety of objects such as tools, footwear, and tires produce markings in their normal usage. Footprints are a common type of impression evidence found at or near crime scenes. Footwear impression evidence and information from the gait pattern may indicate that the subject was walking or running, had sustained an injury or walked with a limp, was possibly intoxicated, had a tendency to walk toe-in or toe-out, or was carrying a heavy object. In connection with all tool marks and suspected tools, it should be remembered that the tool might also have deposited traces in the form of paint, oil, or other materials. Moreover, it is essential for the expert who is to carry out the comparative examination of the tool and the tool marks to understand how the criminal held the tool when making the marks.

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