The author(s) shown below used Federal funds provided by the U.S. 

Department of Justice and prepared the following final report: 

Document Title: Adding Human Expertise to the Quantitative 

Analysis of Fingerprints, Final Report 

Author: Thomas Busey, Chen Yu 

Document No.: 230166 

Date Received: April 2010 

Award Number: 2005-MU-BX-K076 

This report has not been published by the U.S. Department of Justice. 

To provide better customer service, NCJRS has made this Federallyfunded grant final report available electronically in addition to 

traditional paper copies. 

 

Opinions or points of view expressed are those 

of the author(s) and do not necessarily reflect 

the official position or policies of the U.S. 

Department of Justice. 
Final Report 

NIJ grant #2005-MU-BX-K076 

Thomas Busey and Chen Yu, Indiana University 

Abstract 

The goal of this project is to characterize the nature of human expertise using eye tracking 

methodologies, and then use these results to develop and refine quantitative metrics of the 

information contained in friction ridge patterns. Current quantitative approaches to fingerprint 

matching and analysis are not based on human data and therefore do not take advantage of the 

full capabilities of the human visual system. Since humans routinely outperform automated 

fingerprint recognition systems, it is clear that quantitative approaches can be improved by 

adopting some of the strategies that humans employ. However, humans often have difficulty 

describing the results of perceptual processing, and may not even know what information they 

are using. To address this deficit, we used eye tracking to identify what information human 

experts rely on. We constructed a portable eye tracking system that enables us to collect data 

from experts and novices while they perform tasks similar to latent print examinations. Once we 

analyzed the data we obtain a record of the regions visited by the experts as they compared pairs 

of fingerprints. We then developed a series of computational analyses to identify the nature of 

the expertise. This took the form of data reduction procedures on pixel crops from the fingerprint 

images, as well as the development of candidate information metrics that the data from experts 

helps validate. The results demonstrate clearly that human expertise can be inferred from eye 

gaze information through a process of carefully designed studies and hypothesis testing of 

candidate information metrics. Because our candidate metrics take the form of mathematical and 

computational models, they are readily applicable to machine comparison approaches, and also 

can be used to identify the diagnosticity and rarity of particular features in novel prints. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 2 of 53 

Table of Contents 

Abstract .....................................................................................................................................................1

Executive Summary ..............................................................................................................................3

Research Problem ................................................................................................................................................3

Research Designs and Findings......................................................................................................................4

Conclusions .............................................................................................................................................................7

Implications for Practice ...................................................................................................................................8

Introduction ............................................................................................................................................9

A. Eye tracking data processing........................................................................................................9

A.1. Hardware and recording devices ......................................................................................................10

A.2. Pupil and corneal reflection extraction ..........................................................................................10

A.3. Calibration with the scene view.........................................................................................................11

A.4. Monitor corner detection and rectification...................................................................................11

A.5. Trial and event extraction ....................................................................................................................11

A.6. Data cleaning and export ......................................................................................................................11

A.7. Fixation finding .........................................................................................................................................11

A.8. Calibration verification and quantification ...................................................................................12

A.9. System integration...................................................................................................................................12

B. Data analyses .................................................................................................................................. 12

B.1. Fixation statistics......................................................................................................................................13

B.2. Consistency among experts and novices........................................................................................14

B.3. Use of minutiae by experts and novices .........................................................................................15

B.4. Machine translation approaches to data analysis ......................................................................15

B.5. Feature induction from fixation data...............................................................................................17

B.6. Information theoretic approaches ....................................................................................................18

B.7. Linguistic identifiers assigned to extracted features................................................................21

B.8. Summary of data analyses ....................................................................................................................21

C. Conclusions and implications for policy and practice....................................................... 21

D. Products, publications and presentations ............................................................................ 22

D.1. Products .......................................................................................................................................................22

D.2. Publications ................................................................................................................................................23

D.3. Presentations .............................................................................................................................................23

Appendix......................................................................................................................................25

Methods for Latent/Inked comparisons..................................................................................... 25

Stimuli ....................................................................................................................................................................25

Participants..........................................................................................................................................................25

Procedures ...........................................................................................................................................................25

Methods for Inked/Inked comparisons...................................................................................... 25

Stimuli ....................................................................................................................................................................26

Participants..........................................................................................................................................................26

Procedures ...........................................................................................................................................................26

Figures ..........................................................................................................................................27

References...................................................................................................................................52

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Executive Summary 

This Executive Summary is designed to 

provide an overview of the methods, findings 

and conclusions of the project. While it 

provides a summary of the results, many of 

the mathematical and computational 

approaches that are described require 

additional explication in order to gain a 

complete intuition for the methods. The main 

body of the report provides such detail, along 

with supporting figures that demonstrate the 

conclusions in graphical form. Thus the reader 

is encouraged to refer to the full text when 

necessary. 

Research Problem 

Human expertise can be remarkably 

difficult to transfer from one individual to 

another, in part because the tools we have to 

transfer this knowledge, mainly language, are 

sparse and incomplete representations of 

complex perceptual skills. Indeed, 

psychologists differentiate between 

knowledge learning and skill learning, as they 

appear to have different mechanisms 

supported by different brain substrates. This 

should come as no surprise to anyone who has 

ever coached a little league team, taught 

piano, or mentored a latent print trainee. 

Describing what we do is very different than 

actually being able to do it. 

The difficulty we have in translating 

perceptual skills from one person to another 

also implies that the skill that underlies latent 

print examinations may be difficult to 

demonstrate. An examiner can point to a 

region to indicate to another person what 

matches, but they can make the person ‘see’ 

the match in a visual comprehending sense. 

The MagicEye posters popular a few years 

back are a good example of how perception is 

a uniquely personal and private experience: 

Knowing that a dinosaur is hidden in the 

image doesn’t help until all of a sudden you 

can see it completely. 

This kind of perceptual experience can 

be difficult to transfer to others, and this may 

be why judges have been willing to listen to 

defense attorneys who have argued that latent 

print examiners do not have any special 

expertise, and that fingerprints should be just 

shown to the jury. At issue here is the fact that 

much of the process of perception can be 

difficult to translate into language (Snodgrass, 

Bernat, & Shevrin, 2004; Vanselst & Merikle, 

1993) and examinations may be subject to 

extra-examination biases (Dror & Charlton, 

2006; Dror, Charlton, & Peron, 2006; Dror, 

Peron, Hind, & Charlton, 2005). As a result, 

up until now we have been forced to trust the 

word of the examiner that they rely on the 

features they say they do. 

In this project we have used eye tracking 

methodologies, combined with computer and 

mathematical modeling approaches, to infer 

the nature of the expertise in latent print 

examiners. This allows us to address questions 

not only about what information experts rely 

on, but to create candidate models of this 

expertise using mathematical and 

computational approaches that can be 

validated against the human data. No longer 

do we need to simply ask examiners what they 

rely on, which is important because much of 

perception may reside below the level of 

consciousness. For example, the picture in 

Figure 1 illustrates that even though we know 

at a cognitive level that the two lines are 

straight, this knowledge does not allow us to 

perceive them as straight. 

The goal, then, of this project is to use 

eye gaze to identify the features and ridge 

detail information that experts rely on when 

conducting latent print examinations. This 

task is made possible by the fact that the eyes 

contain a region of high acuity, called the 

fovea, outside of which the representation is 

much coarser. Because ridge detail is fairly 

fine, most information must be acquired by 

moving the fovea so that it lands on the region 

of interest. Thus by tracking the position of 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 4 of 53 

the eyes we gain a record of what features or 

ridge detail the examiner considers diagnostic 

or relevant. Such an approach has been used in 

question document applications (Dyer, Found, 

& Rogers, 2008). 

This task is complicated somewhat by 

the fact that the fovea is about 1 degree of 

visual angle, or about the width of your 

thumbnail seen with your arm outstretched (go 

ahead, stick your arm out and see how big this 

is). This area implies that, depending on the 

enlargement of the print, it could encompass 

several different ridges. To gain a final 

representation of what examiners are 

evaluating within this window of high acuity, 

we construct candidate mathematical models 

that we can test between by comparing data 

from experts with data from novices. The 

benefit of these approaches is that we are able 

to characterize the nature of expertise in 

computational form that is easy to implement 

in computer-based matching systems such as 

AFIS. As part of the development of these 

models we will need to describe some initial 

differences between experts and novices that 

will guide our model development. Note, 

however, that systems such as AFIS and 

human examiners have different goals. AFIS 

is designed to quickly return candidate 

matching prints from large stored databases. 

However, human examiners have more 

information available, since they can use 

features such as third level detail. In some 

sense it is desirable to have the two systems 

use different sources information, because 

independent sources give more reliability to 

the final conclusion. Of course, there is a 

potential built-in correlation if the expert only 

looks at prints returned by AFIS. 

Research Designs and Findings 

There are three major research questions 

in this project: 

1) How reliably can eye gaze data be 

collected from experts? 

2) Does this eye gaze data demonstrate 

differences between experts and novices? If 

so, what is the nature of the differences? 

3) How can data from experts 

discriminate between different candidate 

quantitative metrics of the information in 

latent fingerprints? 

These questions take multiple 

approaches to answer, and as a result we have 

combined the Research Designs and Findings 

sections into one, and broken down the 

individual data analyses into subsections 

below. 

Eye Tracking Data Processing 

To gather robust gaze data from 

examiners in the field, we designed a set of 

software tools that would allow us to record 

the video streams from two head-mounted 

cameras, one that monitored the position of 

the eye relative to the head, and one that 

monitored the head relative to the world. By 

performing calibration procedures and 

tracking the corners of the computer monitor 

in the scene video we were able to determine 

the position of the eye on the fingerprint 

images to an average error of less than 1 

degree of visual angle. Note that this is on par 

with commercial eye tracking systems and is 

about the width of the fovea. Therefore, 

accuracy greater than this resolution probably 

is diminishing returns. 

We developed a number of innovations 

in order to reliably capture gaze information 

from videos taken in the field, and our eye 

model innovation is demonstrated in Figure 4. 

We had to deal with all kinds of difficulties, 

such as one recording session when the 

chandeliers from the hotel lobby were 

reflected in the pupil of our subjects. Because 

we can post-process our video we are able to 

recover much of the data that would be lost if 

we had tried to do online eye tracking. 

 We currently have data from 72 experts, 

10 trainees, and 32 novices, which we have 

processed through our software. We ask our 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 5 of 53 

participants to perform abbreviated latent print 

examinations that allow 20-30 seconds per 

pair of images. This allows us to limit the 

recording sessions to 15 minutes to avoid eye 

fatigue, and to obtain eye gaze data from a 

wide variety of images, which is necessary for 

the data reduction procedures. This relative 

brief exposure requires the experts to move 

their gaze to regions that they consider to be 

most diagnostic. 

Figure 8 illustrates the eye gaze data 

collected from one expert, along with the 

fixations that are found by grouping the raw 

gaze data into clusters by computing the 

velocity of the track. It is this kind of data that 

provides the foundation for the analyses 

described in the next section. 

Eye Tracking Data Analyses 

We have three major types of 

experiments that we conduct. The first set uses 

standard latent/inked comparisons from NIST 

special database 27. We also obtained a set of 

clean prints from donors at Indiana University, 

and conducted a set of studies using clean 

images. More recently we have begun to add 

texture noise to clean prints to encourage 

examiners to treat them as latent prints. In all 

cases we ask our participants to determine 

whether the two prints come from the same 

source, although we give a ‘too soon to tell’ 

option as well. Based on these and the fact 

that we know the ground truth allows us to 

compute accuracy. In all cases we find that 

experts outperform novices in terms of overall 

measures of sensitivity. 

In the summaries below we characterize 

the nature of the individual analyses and 

provide a summary of the results. We do not 

provide the raw statistics for each conclusion, 

but refer the reader to the full text for such 

detail. 

Fixation statistics 

As a first step, we compared our experts 

and novices in terms of how often and how far 

them moved their eyes. We found no 

differences between the two groups in terms 

of the amount of time they spend in one 

location, but when experts move their eyes, 

then tend to make shorter jumps, at least 

within one print. 

This behavior is consistent with the idea 

that the information used by experts is not 

individual features to be matched across the 

prints, but collections of features. Since 

collections are much less likely to be 

spuriously matched, they provide much more 

diagnostic value. Of course the expert must be 

able to maintain the entire collection in visual 

memory, and other data from our lab suggests 

that they have longer-lasting visual memories 

(Busey & Vanderkolk, 2005). 

Consistency among experts and novice 

The gaze data provides collections of 

fixations for a given pair of prints for several 

different examiners. We can ask whether 

experts use similar kinds of information by 

looking to see whether they visit the same 

locations on the prints. We used a metric 

called the Earth Mover Distance, which 

computes the similarity between any two 

experts based on how easy it would be to 

move the fixations from one expert onto the 

fixations of another. 

The Earth Mover metric provided some 

surprising results. When applied to 

latent/inked pairs, we found that experts were 

less consistent as a group. This suggests that 

they tended to use different sources of 

information, perhaps by wandering off the 

high-visibility regions of the prints into 

different parts of the noisy areas. The choice 

of which noisy areas to visit may have 

depended on their individual expertise and 

strengths. It will be up to the latent print 

community to decide whether this variability 

is a source of concern. 

We found the opposite result for clean 

image pairs. Now experts are showing more 

consistency as a group, despite the 

overwhelming wealth of ridge detail available 

in clean prints. This is consistent with the 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 6 of 53 

suggestion that experts have an implicit sense 

as a community for which features are most 

diagnostic and tend to fixate them 

immediately. 

Use of minutiae by experts and novices 

One reason that experts produced greater 

consistency as a group with clean prints is that 

they tended to focus on the core and delta 

regions, which are rich in minutiae. Figure 13 

shows the locations of the fixations for experts 

and novices for a clean image pair, and the 

experts tend to send their gaze to regions that 

have much higher minutiae count. We did not 

have enough data to look at individual 

differences between examiners who profess to 

ignore minutiae and those who explicitly rely 

on minutiae, but such an analysis would be 

potentially revealing about the nature of 

individual differences among examiners. 

Machine translation approaches to 

data analysis 

In a separate project we addressed the 

ability of temporal information to determine 

whether two features match across an image 

pair. There have been reports in the eye 

tracking literature that gaze information may 

be more diagnostic than over responding, 

since subjects will often fixate a target one or 

more times before actually identifying it in a 

visual search task. 

We used algorithms adapted from 

machine translation approaches to identify 

corresponding regions in the two prints. Using 

the data from multiple experts, we found that 

the two images could be treated as two 

separate languages, and the pattern of eye 

gaze switches between the two prints acts as a 

translation dictionary. This approach builds up 

correspondences between regions in the prints, 

and the algorithm was much more successful 

in finding correspondences in data from 

experts than data from novices. 

This analysis suggests that gaze data 

alone may be sufficient to determine 

corresponding regions, and that there may be 

more information in the eye gaze channel than 

initially available from the behavioral 

responses of the examiner. 

Feature induction from fixation data 

In order to determine the exact nature of 

the information acquired by experts, we 

cropped out small, 48x48 pixel patches 

centered on each fixation. We then performed 

a data reduction procedure known as 

Independent Component Analysis, which 

seeks to find a common alphabet or feature set 

that best describes the entire dataset. We had 

over 30,000 image crops from experts, and we 

distilled these down to just 120 basis functions 

that, when combined, allow a fairly accurate 

reconstruction of the original image crops. 

This analysis demonstrated fundamental 

differences in the nature of the information 

acquired by experts and novices. While 

novices appeared to focus on fine details in 

high-signal regions, the experts were more 

likely to seek out regions that had more visual 

noise and lower spatial frequency information. 

Information theoretic approaches 

Our most sophisticated computational 

approaches rely on metrics derived from 

Shannon Information Theory (Shannon & 

Weaver, 1949). We trace the ridge elements 

and determine the orientation of the ridge at 

each pixel location. This allows us to collect 

these orientations, as well as the relative 

orientation change, which provides ridge 

curvature information. Figure 19 illustrates 

this procedure. 

The collection of orientations provides a 

measure of the entropy of the patch, and we 

can construct several different measures of 

entropy depending on the assumptions made 

about the frequency distribution. 

We computed the entropy of patches 

extracted at the fixations of experts and 

novices, and determined that some measures 

of entropy distinguished between experts and 

random fixations, while others distinguished 

between experts and novices. This approach 

appears promising in part because entropy can 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 7 of 53 

be based on sources of information than ridge 

orientation. 

We were surprised to find that some 

measures of entropy that we though were 

extremely viable turned out not to 

discriminate between experts and novices, and 

others that we thought would not mean much 

turned out to be very diagnostic. This 

observation illustrates the value that the eye 

gaze data from experts and novices can 

provide in the model generation and testing 

process. 

Conclusions 

We had three main questions that we 

addressed in this project, which we can now 

answer. 

1) How reliably can eye gaze data be 

collected from experts? 

The accuracy of our system, computed 

by comparing the estimated eye gaze position 

against where we asked the participants to 

look, demonstrated that our system performs 

at the same level of accuracy as commercial 

systems. In addition, our post-processing 

procedures and innovative forward eye model 

allow us to deal with field data collection that 

would prove problematic for commercial 

systems. The fact that we built the system 

ourselves based on research articles describing 

different calibration procedures means that we 

understand and trust all aspects of our data. 

Thus the answer to this question is a definitive 

yes, and our results demonstrate that eye gaze 

data can provide strong evidence for the 

nature of expertise in examiners. 

2) Does this eye gaze data demonstrate 

differences between experts and novices that 

would support the continued use of human 

experts as expert witnesses is court cases? If 

so, what is the nature of the differences? 

We found that experts out-performed 

novices in all experiments, both in terms of 

the number of correct identifications and the 

number of correct exclusions. Experts were in 

general less likely to use the ‘too soon to tell’ 

option. 

This behavioral accuracy improvement is 

supported by a number of differences in the 

eye gaze data. Experts had shorter saccades 

between fixations, suggesting that they were 

looking for clusters of features rather than 

trying to match individual features. The 

combinatorial increase in diagnosticity that 

multiple features in combination provides 

makes this strategy potentially much more 

accurate. 

Experts and novices differed in their 

consistency as groups depending on the nature 

of the stimuli. For latent prints, experts 

showed less consistency overall than the 

novices, and we speculated that they were 

venturing into the less visible print regions 

and choosing different regions to visit 

depending on their own individual strengths. 

Novices tended to just focus on the clean 

portions of the latent prints regardless of 

whether there was diagnostic information 

there or not. For the clean prints, however, 

experts showed more consistency overall 

despite the fact that there was a wealth of 

information through the print. This suggests 

that in this case experts have an implicit set of 

features that they acknowledge are most 

diagnostic and tend to fixate these as a group. 

Experts also tended to send their gaze to 

regions with more minutiae. Their fixations 

tended to land in and around the core and 

delta, where there is much more activity that 

creates minutiae. 

Our machine translation procedures used 

the temporal correspondences across subjects 

to identify regions that might match across a 

pair of prints. This technique found many 

more corresponding regions for the experts, 

suggesting that the eye gaze pattern alone can 

reveal portions of the prints that the examiner 

believes matches. 

3) How can data from experts 

discriminate between different candidate 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 8 of 53 

quantitative metrics of the information in 

latent fingerprints? 

We used two complementary approaches 

to address the role that eye gaze data can play 

in discriminating between different candidate 

computational metrics that characterize the 

amount of information in fingerprints. 

The first used data reduction procedures 

on pixel crops taken at the locations of 

fixations. This analysis revealed strong 

differences between experts and novices in 

terms of the nature of the information acquired 

by each group. Experts appear to be acquiring 

information from regions that are noisier, 

perhaps because their training allows them to 

tolerate more noise. They appear to be relying 

on lower spatial frequencies and coarser 

features, suggestive of configural processing 

(Busey & Vanderkolk, 2005). 

The second approach defined several 

candidate entropy metrics using information 

theory and the angles of the ridge elements 

within a window. We found that some entropy 

measures performed better than others at 

discriminating between experts and novices. 

Overall this approach is extremely promising 

because in theory the angles of ridge elements 

provides a complete description of all of the 

information contained in fingerprints. 

Implications for Practice 

Together these results lay to rest the 

suggesting that experts have nothing more to 

add to court proceedings than a novice might. 

While it is true that humans in general have an 

intuitive sense for what features might be 

diagnostic, experts outperformed and looked 

different than novices on virtually all 

measures that we tested. 

From an engineering standpoint, human 

expert eye gaze data provides an important 

validation procedure for any proposed 

quantitative approach to fingerprint matching. 

Human experts have proven to be superior to 

computers in the actual matching procedures, 

in part because they have an ability to alter 

which kinds of information they rely on. If 

Level 3 detail is available, they likely rely on 

it, while they may be able to determine when 

distortions might have occurred and therefore 

not trust ridge curvature as much as they 

might otherwise. This suggests that targeted 

experiments with these particular scenarios 

will help engineers collect eye gaze data that 

would help them train a ‘collection of experts’ 

model in which separate modules each make a 

decision and then a voting scheme is 

performed to make a final decision. 

Experts should be cautioned that these 

differences between groups should not lead to 

complacency. Indeed, special abilities often 

come with vulnerabilities and care should be 

taken to avoid overconfidence. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 9 of 53 

Introduction 

The major goal of this grant is to acquire 

eye gaze data from expert latent print 

examiners in order to determine what features 

or detail they rely on when conducting latent 

print examinations. This information is 

important because experts still outperform 

computers when conducting actual 

examinations (although machines have the 

advantage of vast storage space and can 

provide an initial candidate set for an expert to 

examine). Thus the human expert appears to 

have knowledge that is still not incorporated 

into machine-based approaches. 

To determine which features experts rely 

on when examining latent and inked prints, we 

take advantage of the fact that the human 

visual system has a region of high acuity 

known as the fovea, and detail in regions 

outside this are (the periphery) are represented 

in much coarser detail. Since ridge 

information in fingerprints tends to be very 

fine detail, humans typically must move the 

location of their gaze around on a pair of 

prints, looking for regions that might match or 

definitively not match. By monitoring the 

position of the eye relative to the head, and the 

position of the head relative to the pair of 

prints, we can determine where their gaze 

lands on the ridges and therefore what 

information they choose to acquire. 

Up until this project no one had collected 

eyetracking data from latent print examiners. 

One reason for this is that experts tend to be 

located in crime labs, away from research 

facilities. Eyetracking equipment is typically 

large not portable, often taking whole rooms 

in research labs. A major initiative of the 

current program has been to develop a 

portable, research-grade eye tracker that could 

be used to reliably collect data from expert 

examiners. This hardware/software package, 

which we have made open-source, is termed 

ExpertEyes (a play on the word ‘expertise’). 

This package is a major contribution of the 

present grant, and we will describe its 

functionality in section A below. We currently 

have data from 72 experts, 10 trainees, and 32 

novices, which we have processed through our 

software. 

Once the eyetracking data has been 

processed, we have a record of where the gaze 

falls on the prints, for each point in time. This 

data is very simple in form, because it is just 

the x-y locations in time. However, it is also a 

very rich dataset, which can be related to the 

features of the print, or other measures. We 

discuss the analysis of the eye tracking data in 

section B below. 

Our rational for this research comes from 

the observation that no systematic study of 

expertise in latent print examiners has been 

conducted using eye gaze methods. Thus it is 

not known whether the experts are using 

features they say they are using. Indeed, there 

is evidence from radiology that expert 

radiologists fixate on regions other than what 

they say they are looking at (Krupinski, 1996). 

Because we have two major sets of 

accomplishments, one related to data 

collection, and the other related to data 

analyses, we discuss these in two separate 

sections, and within each section we include 

the methods, results and conclusions from 

each section. We have also discussed the 

relevant literature within each section. 

A. Eye tracking data processing 

Why not just ask examiners what 

features they rely on? One issue with this 

approach is the fact that perception is often 

difficult to express in language and many 

perceptual processes take place at a level 

below consciousness (Snodgrass, et al., 2004; 

Vanselst & Merikle, 1993). An easy 

demonstration of this is the perceptual illusion 

in Figure 1. The red lines are actually parallel, 

but knowing this fact does not allow observers 

to change their perception of the two lines 

warping. However, the edge of a sheet of 

paper reveals that the lines are actually 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 10 of 53 

straight. This illustrates that conscious 

processes do not easily alter the output of the 

perceptual system, and that we do not have 

access to the basic levels of perception at the 

level of awareness. Many experts report 

having a flash of recognition or insight, and 

only then go back and document features that 

they now perceive as matching, a phenomenon 

known as the “Eureka” effect in the Cognitive 

Science literature (Ahissar & Hochstein, 

1997). 

How can we access the information that 

experts use, when the expert may not be 

consciously aware of what they rely on? How 

can we implement a quantitative approach 

(e.g. a cognitive model and/or a computer 

program) to extracting features that we don’t 

know? Our solution is to rely on collecting 

fine-grained behavioral data from experts in 

combination with tailored experiments and 

computational modeling to infer the set of 

features that characterize the information 

content in friction ridge impressions. 

A.1. Hardware and recording 

devices 

After a survey of the marketplace we 

determined that no commercial eye tracker fit 

our needs of high resolution and portability. 

We need to collect data at latent print 

conferences like IAI and the Daubert Las 

Vegas seminar where we have access to large 

numbers of potential participants. We needed 

a portable system that is tolerant to different 

lighting conditions, as well as eyeglasses since 

good examiners tend to be older and require 

glasses. We also required a system that had 

good spatial resolving power, since the 

features we thought experts might rely on 

could be as small as a single ridge. 

As no off-the-shelf system met these 

needs, we designed our own eyetracking 

system. It consists of two cameras, one of 

which records the scene and therefore the 

viewer’s location relative to the image, and 

the other which records the position of the eye 

relative to the head. Through some calibration 

procedures we can recover the position of the 

eye relative to the fingerprint image. The 

system is portable, lightweight, and quickly 

adaptable to different subjects for rapid data 

acquisition at conferences. 

Rather than try to analyze eye position in 

real time, we simply recording the video 

streams of the eye and scene cameras. We can 

take advantage of offline processing, which 

allows data processing that is extremely 

precise. We spend about 12 seconds 

processing each frame of a 30,000 frame 

movie that represents 15 minutes of data 

collection. Based on data collected during our 

calibration procedures, we reduce our location 

estimation error to around 1° of visual angle, 

which is about the size of the fovea and 

therefore represents close to what is 

meaningful since the human is acquiring 

information from the entire region contained 

within the fovea. We further improve our 

spatial resolving power by enlarging the 

fingerprint image on a 21” LCD monitor, thus 

making each feature large relative to the 

fovea. 

Our eye tracker consists of a set of 

cameras mounted on a set of safety glasses 

(see Figure 2). This set of cameras records the 

position of the head and eye at 30 frames per 

second, which is suitable for a task in which 

most of the fixations are fairly stable. 

A.2. Pupil and corneal reflection 

extraction 

To illuminate the eye, we shine an 

infrared LED on the eye from a position near 

the eye camera, which is pointed at the eye. 

This results in a bright spot appearing on the 

cornea, called the corneal reflection. The 

relation between this spot and the pupil varies 

systematically as the eye changes orientation. 

The first step is to identify the pupil and 

corneal reflection. While a variety of 

techniques have been proposed, we created a 

novel procedure in which a forward eye model 

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and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 11 of 53 

is created by drawing dark and light ovals 

over the eye image and adjusting the position 

of the two to find the best match. Figure 3 

illustrates this procedure, while Figure 4 

demonstrates the software that fits the forward 

eye model. 

These adjustment procedures are quite 

time-consuming, and take 6-8 seconds per 

frame to compute. Thus it can take several 

hours on an 8-core computer to fit the model 

to all 30,000 frames for each subject. 

A.3. Calibration with the scene 

view 

To make the link between the scene 

camera and the eye camera, we ask 

participants to look at black dots on a white 

screen. These appear for about 5 seconds and 

then move to another location. The 

ExpertEyes software contains a module that 

allows us to link an eye position with a 

position in the scene camera. The user selects 

a frame where the participant is assumed to be 

looking at the black dot, and then indicates the 

location of the dot in the scene camera. The 

computer then finds this dot in several 

subsequent frames to reduce noise. 

We then fit a two-dimensional 

polynomial function that relates the u-v 

location of the pupil to an x-y location on the 

scene camera. 

A.4. Monitor corner detection and 

rectification 

The second stage of analysis requires 

that we identify the location of the monitor in 

the scene camera. We apply a barrel distortion 

correction algorithm to each scene camera 

image and then have the user click on each of 

the four corners of the monitor in a 

thresholded scene image. We then use Gabor 

jets, which are an adaptive template match 

algorithm, to identify the corners in the rest of 

the images. Figure 5 demonstrates how we 

correct for the barrel distortions that all 

cameras induce, and Figure 6 illustrates the 

procedures by which we search and identify 

the corners. The search space is found for each 

corner by a human coder using a shuttle-jog to 

quickly mark the search area for the entire 

movie for each corner. 

Once we know where the eye is in the 

scene view for a particular frame, as well as 

where the corners of the monitor are in the 

scene view, we can then interpolate the eye 

position back into the coordinates of the 

monitor. This gives us the position of the eye 

on the images that contain fingerprints. 

These procedures provide the ultimate 

goal of the eye tracker: the position of the eye 

on the fingerprint ridge detail. We can then 

tell, with accuracy that is about the size of the 

fovea, which information the expert is using 

when performing examinations. 

A.5. Trial and event extraction 

To obtain enough information about the 

diagnosticity of different kinds of information, 

we often conduct experiments with 30-40 

images, each shown for 20-30 seconds to 

encourage the examiners to focus on only the 

most diagnostic features. The ExpertEyes 

software contains a module that automatically 

extracts event information and allows human 

verification and correction. 

A.6. Data cleaning and export 

Eyetracking data invariably has some 

missing data, usually when the participant 

moves their head enough so that a corner of 

the monitor moves off the edge of the scene 

camera. Figure 7 illustrates the procedures that 

allow the user to identify and mark regions of 

the data that are bad. We typically throw out 

less than 5% of our data. 

A.7. Fixation finding 

The final step in the analysis process is 

to find fixations. The eye tends to move 

ballistically from one location to another on 

static images, with dwell times that average 

about 300 ms in duration. During this dwell 

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and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 12 of 53 

period the eye experiences micro-saccades, 

tiny movements that prevent the visual world 

from fading. However, these micro-saccades 

are typically not meaningful from an analysis 

standpoint, and there is also jitter from error in 

the estimation procedures. Thus to smooth the 

data we rely on fixation finding routines that 

perform a cluster analysis that groups similar 

eye gaze locations into fixations and saccades. 

We have developed our own algorithm 

of eye fixation finding which is composed of 

five step: 1) First, we compute the magnitude 

of velocity from raw eye movement data (x,y); 

2) we next use a pre-defined threshold to 

segment the whole continuous stream into 

several big segments that correspond to 

dramatic eye location changes; 3) we zoom 

into each big segment and re-segment each 

into individual segments that correspond to 

small eye position changes. Those small 

segments may or may not correspond to 

fixations; 4) Finally, we took spatial 

information into account by merging small 

segments (detected from Step 3) if they are 

spatially close to each other (e.g. eyes moving 

around an area with a certain speed). After 

the above five steps, we typically are able to 

successfully segment a continue eye 

movement stream into several eye fixations by 

integrating both temporal (the speed of eye 

movement, etc.) and spatial (the overall spatial 

changes of eye gaze location) information. 

 Figure 8 shows the raw eye trace along 

with the results of the fixation finding 

algorithm. The parameters are tuned by eye, 

since no objective criterion for fixations and 

saccades exists in the literature. 

A.8. Calibration verification and 

quantification 

An all-important task is to identify 

whether our calibration procedures accurately 

measure eye position. To ensure this, we ask 

our participants to perform an additional 

calibration procedure at the end of the 

experiment. We ask them to look at known 

locations on the monitor and then verify 

whether we can accurately track their gaze. As 

Figure 9 illustrates, we typically find that our 

calibration accuracy is quite high, comparable 

to commercial systems. In many respects our 

system is superior to commercial systems 

because we are able to go back and re-fit the 

eye model if the parameters are incorrectly 

specified. If we were recording live from the 

field we would simply not be able to use that 

data. Given how valuable the data is from 

experts, we are fortunate in that we are able to 

make full use of almost all of the data we 

gather from examiners. 

A.9. System integration 

The ExpertEyes software integrates four 

major modules: eye calibration, trial marking, 

data cleaning and eye model fitting. Figure 10 

illustrates the main screen of the software that 

integrates these different modules for one 

subject’s data. 

B. Data analyses 

The analyses of eyetracking data have 

subsumed much of our research efforts for the 

latter portion of the grant. Perfecting our eye 

tracker to enable research-grade data required 

an iterative procedure of testing and 

development, and the result is that we are 

much further along with the software tools to 

gather eye tracking data than we are with the 

data processing. The data analysis is 

complicated by the fact that there are several 

major classes of approaches that can be used 

to identify the nature of expertise in latent 

print examiners. 

Below we describe the six different types 

of analyses that we have conducted. Note that 

each of these approaches relies on 

sophisticated mathematical modeling and 

machine learning techniques, which are quite 

time consuming to implement and test. Thus 

the results below represent in many cases 

completed analysis, but also signposts to 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 13 of 53 

future analyses as is often the case in the 

hypothesis development and testing loop of 

science. The first three sections below are 

described in Busey et al. (Submitted 

Manuscript) and portions of the text below 

also appear in the submitted manuscript. The 

methods of data collection are presented in the 

appendix of this Final Report. 

We compared the behavioral results 

found a mean d’ for experts of 2.47 and a 

mean d’ for novices of 1.30. Thus experts 

seem to be able to reliably out-perform 

novices and have fairly high d’ values even 

with short viewing times. The experts had a 

false alarm rate of 0.007 (or .7%) while the 

novices had a false alarm rate of .25. These 

numbers are based on relatively small number 

of participants, but it does indicate the rather 

large accuracy difference between the two 

groups. 

B.1. Fixation statistics 

The first step in analyzing eyetracking 

data is to compute various summary statistics 

that are likely to be related to the strategies 

used by experts and novices. These are 

essential to determine whether the eyetracking 

data contains the kind of structure that will 

allow us to extract information about the 

quantitative information content as revealed 

by eyetracking scan paths. 

At each point during the experiment, the 

eyes are at a location on the latent/inked print 

pair. Eyetracking researchers distinguish 

between two main states of eye position: 

fixations and saccades. Fixations are periods 

where the eye is relatively stable on the image, 

and tends to last between 200 and 500 

milliseconds. Saccades are periods where the 

eyes move rapidly from one region to another 

for a period of around 20-170 ms. Note that 

these two categories characterize a dataset that 

is often more continuous, since the eyes can 

move slowly and smoothly at times as in 

smooth pursuit of moving objects or when 

experts follow a ridge. These, however, tend 

to be second-order effects and there are a 

number of accepted techniques that are used to 

decide whether the eye is currently in a 

fixation or saccade state. 

Once the fixations and saccades have 

been identified, we can compute statistics 

about the expert and novice groups. We had 

no prior expectations for these data, since this 

kind of data has not been collected before. 

Perhaps somewhat surprisingly, experts and 

novices spend the same proportion of time 

looking at latent and inked prints (about 67% 

on latent) and the mean fixation duration is 

about the same on the latent image, for about 

400 ms. Both groups made longer fixation 

durations on the inked print, by a factor of 

50% more. Thus for these two groups, the 

statistics are quite similar, and may reflect 

low-level eye movement programming 

behavior that is shared by all human 

observers. 

We found that experts and novices have 

equal numbers of saccades (F(1,11)<1) but the 

experts had much shorter saccades than 

novices both on the latent print side ( 66.3 vs. 

100.7 pixels, F(1,11) = 9.0, p<0.05) and on the 

inked print side (57.5 vs. 99.8 pixels, F(1,11) 

= 12.3, p<0.01). These results are consistent 

with experts making smaller eye movements 

to regions that are close together. 

In addition, experts make an average of 

58.9 saccades within a fingerprint vs. 24.7 for 

novices. So while experts make more saccades 

than novices, they still find a way to suppress 

saccade length with a mean length of 171.8 vs. 

191 pixels for novices. One possibility is that 

experts might know the layout of fingerprints, 

allowing them to encode information at highly 

diagnostic fixations and reduce extraneous 

saccade. Also, experts are able to make more 

contiguous fixations on one side before 

switching (mean 5.46 vs. 4.32 for novices). 

All of this converting evidence suggests that 

they approach the task of print matching in a 

different way than novices and may intuitively 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 14 of 53 

understand which features are most 

diagnostic. 

To validate these candidate hypotheses, 

however, we have to look at the locations 

visited by experts, and we rely on a 

comparison across observers to look for 

consistency within groups. If experts rely on a 

common feature set and quickly identify the 

most informative regions in a print pair, we 

should see common regions visited by all 

experts. This is discussed in the following 

section. 

B.2. Consistency among experts 

and novices 

To visualize the regions visited by 

experts and novices, we create a visualization 

called a heat map by accumulating the 

duration spent at each location and 

representing this information as color applied 

transparently over the print pair. Figure 11 

shows two example print pairs from two 

participants, one expert and one novice. 

These heat maps represent regions that 

were visited for longer durations as hotter 

colors. As can be seen in Figure 11, the expert 

tends to focus their attention on a few regions, 

while the novice tends to distribute their gaze 

across disparate locations. 

We noticed that this was a general trend. 

Not only did experts tend to focus on fewer 

regions, they tended to be the same regions 

across experts for a print pair. 

To confirm this, we computed a 

consistency analyses. This relies on a 

statistical algorithm known as the Earth 

Movers Distance. To gain an intuitive sense of 

this comparison technique, imagine that one 

expert’s heat maps represent piles of dirt, with 

hotter colors representing taller piles. A 

second subject’s heat map represents holes in 

which dirt can be added. The Earth Mover’s 

Distance computes the shortest path that a 

dump truck could take to move the dirt from 

the piles to the holes. If two experts have very 

similar maps then this is a very short distance 

since the piles and the holes align. However, if 

two subjects (say two novices) have very 

different heat maps, the distance to move the 

‘dirt’ from one subject to another will be quite 

large. In essence this quantifies in a formal 

way the intuitive procedure of printing out 

both heatmaps on transparencies and holding 

them up to see if they align across subjects. 

We performed this analysis with both 

latent and inked prints. With latent prints, of 

the 90 trials in the experiment, experts were 

more similar to other experts on only 29 trials, 

while on the remaining 61 trials the novices 

were more consistent with each other. We 

computed the mean of the inter-expert 

distances, which was 48.4. The mean of the 

novices was smaller at 45.04. A paired t-test 

computed across the trials was significant 

(t(89) = 2.19; p < 0.031). 

With inked prints, we found the opposite 

results. We again computed the earth mover 

distance for each trial (including each of the 

individual presentations within each trial) 

between each expert and every other expert, as 

well as between each novice and all the other 

novices. However, contrary to the latent/inked 

prints, we find that now the experts show 

much more consistency than the novices. 

Experts had smaller distances to other experts 

on 78 trials and novices had smaller distances 

to other novices on only 12 trials. Experts had 

an average distance to other experts of 133.2, 

while the average distance of novices to other 

novices was 196.4 (F(1,178) = 97.9; p<0.001). 

This suggests that experts have an intuition for 

which areas are likely to be the most 

diagnostic, and given high quality prints they 

are able to quickly move to those areas and 

begin to acquire information. 

Implications for training and practice 

The consequences of greater or less 

variability is something that we believe the 

community should discuss. It is entirely 

possible that a consistent conclusion could be 

reached by examiners using different sources 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

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Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 15 of 53 

of information. However, it may be troubling 

from a legal or policy standpoint to have 

different examiners relying on different 

sources of information. Until we have an 

independent information metric (discussed 

later), we do not have direct comments on this 

issue. We do believe that issues of consistency 

should be discussed by professional 

organizations. It is possible that it did not 

appear as an issue until now due to the lack of 

eyetracking data. 

It will also be important for the 

community to continue to monitor the state of 

eyetracking research. The present results are 

based on a relatively few numbers of subjects, 

and more data may refine the conclusions 

about which group has more variability. 

B.3. Use of minutiae by experts 

and novices 

Minutiae provide the foundation for 

many strategies of latent print identification, 

and may be used implicitly by examiners even 

if they do not explicitly rely on these sources. 

Experts might be more or less likely to visit 

regions that include minutiae. If the novices 

all tended to gravitate toward a cluster of 

high-quality inked prints and the experts 

tended to focus instead on non-minutiae 

information such as ridge orientation and 

inflection, we might find differences in terms 

of the number of minutiae each group visits. 

To assess this computationally, we 

defined a circle of arbitrary size (we chose 50 

pixels but the conclusions do not depend on 

this value) and centered this circle on each 

fixation. We then counted the number of 

minutiae inside this circle, as illustrated by 

Figure 12. 

We found that experts tended to have 

many more minutiae near each fixation. 

Experts had an average of 1.50 minutiae near 

each fixation, while novices had only an 

average of 1.16, which is a significant 

difference. The 95% confidence interval based 

on the null hypothesis of no difference 

between the two groups is [-0.24, 0.26] which 

does not include the actual difference of 

0.344. 

Figure 13 demonstrates why experts have 

so many minutiae near each fixation. The 

expert fixations are in red plus signs, and the 

novices are in magenta asterisks. On this 

particular trial the experts had 1.42 minutiae 

near each fixation, while the novices had only 

1.05, which is a statistically significant 

difference. The novices tended to look at the 

top portion of the print, which has relatively 

few minutiae. The experts tended to look at 

the core and lower-right regions, which tend 

to have much more movement and activity in 

the ridges, leading to more minutiae. This 

practice will lead to not only looking at more 

minutiae, but also greater consistency among 

experts if they all follow a similar pattern of 

looking in these high-density regions. 

B.4. Machine translation 

approaches to data analysis 

The major goal in this type of data 

analysis was to integrate data from human 

experts with statistical machine learning 

algorithms to provide a quantitative analysis 

of the information that was available in inked 

and latent prints. We argue that the 

quantitative evaluation of the information 

contained in latent and inked prints can be 

vastly improved by using elements of human 

expertise to assist the statistical modeling. 

The eye tracking technique allowed us to 

monitor the expert’s momentary gaze 

moments when s/he is engaged in examining a 

fingerprint. We argue that eye movement data 

indicate what participants visually attend to 

when they examine a fingerprint image. Thus, 

participants may not know explicitly their 

matching algorithms or strategies, and they 

may not be able to formally describe what 

they are doing. However, their eyes are 

moving rapidly in our task and actively 

collecting the information for the brain. Every 

single eye movement indicates what 

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Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 16 of 53 

information is required in the internal 

computation. 

We have recruited two groups of 

participants in our study – an expert group (n 

= 6) and a novice group (n = 6). Each 

participant in each group was asked to 

examine 30 ink and latent print pairs display 

simultaneously on a computer screen. In each 

trial, they were asked to make a judgment 

whether the latent print in the current trial 

matches with the ink print. Besides this task, 

there was no constraint how long they may 

take to make this decision, and what visual 

features they should pay attention to. We used 

a Tobii eye tracker to monitor their gaze at a 

frequency of 50Hz. In total of 30 trials, we’ve 

collected 45,000 data points per participant. 

The goal of our data analysis was to 

demonstrate potential differences between 

novices and experts. We intended to find the 

differences between experts and novices based 

on their eye movements. This aim was 

particularly challenging given the following 

observations: 1) Different experts may 

visually attend to different subsets of features 

and used them to perform the matching task; 

2) Novices may visually attend to most salient 

areas in both inked prints and latent prints, 

which may contain important features; and 3) 

Different prints may pose different challenges 

to individual participants. 

Our solution was based on advanced 

techniques in machine translation. Briefly 

speaking, given two sets of text in parallel (for 

example, one in English and one in French), 

current machine translation techniques can 

compute which words in English maps to 

which words in French. Similarly here, if we 

treated the areas of interests in the ink and 

latent prints as two languages, then we can 

compute which region in one image is mapped 

to which area in the other image. Our 

hypothesis was that experts might be able to 

identify more such pairings while novices 

might not be able to do so. 

Given a fingerprint-matching task, 

subjects (either fingerprint experts or novices) 

generate multiple eye movements on both the 

ink print and the latent print displayed 

simultaneously on the computer screen. We 

are interested in visual features that are used 

to make a decision. To do so, we need to find 

which eye movements on the ink print 

correspond to which ones on the latent print. 

The correspondences can be potentially 

estimated based on two streams of eye 

movements that jointly determine which inklatent patch pairing is relevant. As shown in 

Figure 14, we can conceptualize patch-topatch mapping as a translation problem - how 

to cognitively translate image patches in an 

ink print into image patches in a latent print. 

Thus, the learning mechanism we propose 

rests on advances in machine translation. 

Briefly, machines ‘learn’ word 

correspondences (which word in one language 

corresponds to which word in another 

language) by finding statistical regularities 

across large parallel corpora in two languages. 

Here, we use a similar computational 

approach with eye movements in latent prints 

as one language, and eye movements in latent 

prints as the other. This conceptualization 

provides a unique way to discover patch 

correspondences. More specifically, the 

learning process can be formalized as an 

expectation-maximization algorithm (EM) 

(Dempster, Laird, & Rubin, 1977). The idea 

of EM is that there is a way of representing 

the data as the sum of component probability 

distributions. More specifically, the 

probability of an ink patch is expressed as a 

weighted mixture of the conditional 

probabilities of the latent patches. The goal of 

our data analysis algorithm is to find those 

reliable correspondences that maximize the 

likelihood function of observing the whole 

data set. The method computes association 

probabilities of all the possible patch pairs 

simultaneously. 

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Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 17 of 53 

This technique not only reveals the 

corresponding regions between latent and 

inked prints, it also simultaneously estimates 

the features used to make these 

correspondences. The first step in this process 

is to identify clusters of fixations and use the 

temporal sequences of fixations to determine 

correspondences between the latent and inked 

prints. Figure 15 shows an example from an 

expert. The white lines link and highlight 

corresponding areas in two images identified 

by two experts, which is very similar to each 

other.

Figure 16 shows two major results that 

come out of this analysis. First, experts 

identify far more corresponding areas between 

latent and ink prints (m=12.3) compared with 

novices. This confirmed our hypothesis that 

fingerprint expertise can be demonstrated by 

their fine-grained behavioral data in real-time 

examination. More interestingly, we also 

found a reliable consistence of the area pairs 

identified across by different experts. 

Moreover, Figure 16 showed individual 

differences with novices. We found an outlier 

subject #4 in this group. It turned out that 

subject #4 was a graduate student in the PI’s 

lab and has extensive experiences with 

fingerprint and interactions with fingerprint 

experts. Interestingly, even that subject didn’t 

get a formal training to be a fingerprint expert. 

Through daily research experience with 

fingerprints, this individual also acquired 

some expertise that is comparably significant 

compared with other novices. 

B.5. Feature induction from 

fixation data 

The machine translation approach takes 

advantage of contextual information when 

matching regions or clusters across observers. 

If two or more observers tend to jointly fixate 

two regions across a print pair, the algorithm 

will identify these correspondences, much like 

hearing an unknown word in the context of 

known foreign words help establish the 

meaning in terms of know English words. 

These analyses do not have access to the 

underling feature information; only the 

fixations from experts and novices along with 

the x and y coordinates of the fixations. To 

take full advantage of the feature information 

available when linking the fixations back to 

the ridge detail, we have extracted out small 

windows of features centered on each fixation, 

like those shown in Figure 17. 

We then collect all such image windows 

from all 6 of our experts on the clean images 

and perform several different kinds of feature 

induction analyses. We have included the data 

from 25 experts across 4 different 

experiments, giving a total of over 38,000 

image windows from 270 unique fingerprints. 

We also included some 32,000 fixations from 

18 novices on the same images. We analyzed 

the data separately for the two groups in order 

to make comparisons across the groups in 

terms of the features they rely on. 

Our first data analysis relies on a 

technique called independent components 

analysis (ICA). This approach relies on 

looking for spatial correlations, much like 

principal components analysis, but with the 

orthogonality constraint relaxed. We extract 

out a set of features that forms the 

fundamental alphabet for image 

representation. 

The left panel of Figure 18 illustrates the 

results of one such analysis. It shows a large 

set of features derived from ICA that represent 

the basic features used by examiners. The 

image patches were rotated vertically prior to 

data reduction, which account for the fact that 

the majority of energy is in the vertical plane. 

The individual features are grouped together 

by similarity to demonstrate how the 

algorithm can handle spatial uncertainty. 

The right panel of Figure 18 illustrates 

the feature set derived from the novices. 

Despite an approximately equal number of 

image patches in the two datasets, there are 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 18 of 53 

striking differences between the two sets of 

recovered features. The experts appear to rely 

on features that are of coarser scale, which 

would be consistent with the use of integration 

over greater spatial extents (see (Busey & 

Vanderkolk, 2005) for more on configural 

processing in experts). The novices appear to 

rely on finer regions that are more noise-free. 

These regions may not be as diagnostic as 

more noisy areas, since the identification task 

sometimes requires obtaining information 

from regions with poor signal to noise ratios. 

We can use these derived filters in 

several different ways. The feature set can be 

used as a basis set similar to that used in 

image compression (Portilla, Strela, 

Wainwright, & Simoncelli, 2003). We have 

begun to ask whether the feature set for 

experts is easier to compress using the fixed 

120-element feature set than the novice set. If 

it were, this would suggest that experts have 

an implicit agreed-upon set of features that 

they naturally gravitate to. 

The basis set also allows us to 

characterize the high-dimensional space of 

each patch (each patch has 48*48 or 2304 

dimensions) in a much lower, 120 dimensional 

space. This space simply indicates the 

contributions of each of the 120 basis features 

to each patch. The reconstruction will have 

some error, but the redundancy seen with the 

images in Figure 18 suggests it will be 

minimal. This low-dimensional representation 

then allows us to compute the relative base 

rates of different types of features, expressed 

in terms of their position in the 120 

dimensional space. If we have a feature that 

falls in a relatively sparse region in this space 

(acknowledging that most things are in sparse 

regions in a 120 dimensional space) this might 

suggest that this may be particularly 

diagnostic or rare. 

B.6. Information theoretic 

approaches 

We can use the eye tracking data derived 

from experts to help test candidate 

information quantification approaches. The 

idea is to create several candidate measures of 

information and use the data from the experts 

to determine whether they tend to fixate on 

regions that have more information by a 

particular metric. If so, we can then use this to 

begin to explore extensions to the metric to 

determine how it might be extended and 

improved. Once we have a revised metric, we 

can then use to suggest changes that the 

experts might make to their procedures, or 

provide base-rate information on particular 

types of features. Thus the refinement of a set 

of metrics and testing against human data is a 

feedback loop that can be used to improve 

both human performance and measures of 

information in fingerprints. 

All measures of the information content 

in friction ridge impressions must first create a 

feature set. For example, many current 

approaches rely on the locations and 

orientation of minutiae (Egli, Champod, & 

Margot, 2007; Neumann, et al., 2007; Su & 

Srihari, 2008) or ridge elements (Su & Srihari, 

2008). These approaches have real strengths, 

in that they are easily identified and face 

validity in that experts report using minutiae 

and relative locations of ridges. They also 

allow statistical approaches that model the 

variability within different impressions of the 

same finger as compared with other prints 

(Neumann, et al., 2007) and provide 

generative models that can be used to 

characterize the probability of any one 

particular print relative to the entire database 

(Srihari & Su, 2008). However, minutiae and 

ridge endings may represent only a subset of 

the information used by experts, even if the 

print is too noisy for third level detail. Indeed, 

there is a published report of a small friction 

ridge impression that was individualized 

despite the lack of any ridge ending or 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 19 of 53 

bifurcation minutiae (Reneau, 2003). Thus, 

the feature set used by experts may contain 

many different sources of information, only 

some of which is captured in current 

quantitative approaches. 

Engineers have tried many different 

techniques to quantify the information 

available in fingerprints, including minutiaebased systems, ridge flow and even level 3 

detail. However, the development of each of 

these systems tends to ignore the expertise that 

resides in the latent print examiners’ visual 

systems, which have proven to be much better 

than computer recognition systems. We are 

using our eye tracking data to validate 

candidate information metrics, under the 

assumption that experts will move their eyes 

to regions they deem most diagnostic or 

contain the most information. Below we 

describe one set of metrics based on 

information theory, and then describe the 

validation process. 

One possible metric for measuring 

characteristics of locations being looked at by 

finger print examiners is the amount of 

information contained in the locations. In this 

analysis, we focus on the ridge edge direction 

information being taken in by the examiners 

through their eye fixations on a print. Here, 

we use Shannon information theory (Shannon 

& Weaver, 1949) as the measure of the 

information. The detail of how we perform a 

measurement is as follows. 

As shown in Figure 19, we extract 

fingerprint ridge orientation information and 

create the enhanced fingerprint image using 

Peter Kovesi’s implementation1

 of the 

technique proposed by (Hong, Wan, & Jain, 

1998). Then ridge edges are detected from the 

enhance fingerprint image using boundaries 

detection function (bwboundaries) from 

MATLAB Image Processing Toolbox. We 

then work along the individual ridges in the 

 1

http://www.csse.uwa.edu.au/~pk/Research/MatlabFn

s/index.html 

print and encode the orientation of each ridge 

edge element into strings. In order to use 

Shannon theory, the orientation is rounded off 

to a full degree (so we have 181 symbols 

representing 0 to 180 degrees to use for 

encoding a string). The following equation is 

then used to compute information amount 

presented: 

€

I = − p(xi)log2 (p(xi))

i=1

n

∑ (1) 

where n is the total number of edge elements. 

p(xi) is the probability that the orientation of 

the edge element i appear in the observed area. 

Using above metric, we investigate the 

amount of information taken in by expert and 

novice fingerprint examiners. Given a 

fixation point on the fingerprint, we 

approximate the area observed by taking a 

square patch around the fixation, a 50x50 

pixel area in a 1050 x 1680 pixel fingerprint 

image in our experiment. 

In order to visualize the possible 

successes and limitations of each candidate 

information metric, we have developed a 

color-based visualization that illustrates the 

amount of information contained at each 

location as determined by a particular 

algorithm. These Figures are found at the end 

of this report due to the large size of the 

figures. 

Figure 20 and Figure 21 illustrate two 

information metrics, where the amount of red 

in the visualization display implies a greater 

amount of potential information available. The 

relative information metric in Figure 20 seems 

to correspond to what intuitively seems 

informative, mainly regions of minutiae, as 

well as large changes in curvature. Figure 21 

also shows these regions, with the addition 

that this metric may not discriminate as well 

between high and low information regions. 

However, whether these metrics are actually 

useful depends on whether they correspond to 

whether they are fixated by experts. If so, we 

can begin to refine our information metric. 

However, if we don’t see differences between 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 20 of 53 

experts and novices, or between experts and 

random fixations, we will need to abandon 

this particular set of information metrics and 

look to other possibilities. 

The validation procedures with experts 

and novices are described next. This is an 

important step and highlights the value of our 

expert data. Rather than simply coming up 

with a metric that we think might work, we 

verify that experts might intuitively use such a 

metric, even if they do not explicitly calculate 

the probabilities. Instead, we are modeling 

processes in the visual system that may 

approximate these calculations. 

Information Use by Experts and Novices 

A reasonable approach to testing various 

metrics is to determine whether experts rely 

on the regions determined to be most 

informative by a particular metric. One could 

simply ask experts what information they use, 

but often they have difficulty explaining what 

regions or information they actually use. This 

results from the fact that much of perception 

resides below the level of consciousness, and 

can be difficult to explain in the sparse code of 

human language. 

We compute the information contained at 

each location for each subject, and then 

average these across trials for each subject. 

We are interested in whether experts tend to 

visit regions with different information values 

than novices do. 

Figure 22 and Figure 23 show the 

fixations from two experts, superimposed over 

a relative information metric based on the 

difference between two consecutive angles. 

The fixations appear to fall in regions that 

have higher information content. Figure 24 

and Figure 25 show the same information 

metric applied to data from two novices. Here, 

the fixations seem to fall in regions that have 

lower information content. 

To test these observations, we compared 

the average information value visited by 

experts and compared this with that visited by 

novices. We found that three metrics reliably 

differentiated between experts and novices 

(t(11) = 2.88; p = 0.015). We computed the 

information at each fixation of the experts and 

averaged these across the fixations for all 

trials in which individual images were shown. 

Experts reliably visited regions that had lower 

values of entropy, meaning that these were 

regions that tended to have more variable 

ridge detail. This was true whether entropy 

was calculated along a ridge line, or just 

averaged over all pixels in a region around the 

fixation. 

We also found that experts rely on much 

more informative regions that one might 

expect on average. We computed the entropy 

values at random locations, taking X and Y 

values from different fixations to ensure that 

we drew entropy values from valid locations. 

Experts performed reliably better on many 

measures, including the standard entropy 

measure described above (t(11) = 2.81; p = 

0.017). In fact, the novice data looks very 

similar to the random data, suggesting that 

novices may not have a clear idea of where to 

look in the image. 

These results validate our entropy 

measure as an approximation to the kinds of 

information that experts are using when 

matching latent to inked prints. This is an 

important step because this metric quantifies 

the total information available in fingerprints, 

given our particular definition of information. 

Not all metrics proved diagnostic. We 

had high hopes that the relative angle 

measure, computed by taking the difference of 

the angles along a ridge, might also be 

diagnostic. However it did not reliably 

differentiate between experts and novices, 

suggesting that experts may not rely on this 

information. 

Additional Entropy Analyses 

In addition to the analyses described 

above, we have to other analyses that we have 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 21 of 53 

begun to explore. The analyses of these 

metrics are ongoing. 

If experts can reliably match a region on 

the latent with a region on the inked print, 

they may do so because the two patches have 

similar entropy values. This suggests that we 

can compute the combined entropy for the two 

patches across a saccade that bridges the two 

prints. We should find that these entropy 

values are reliably different than ones 

computed from novices across saccade pairs. 

This analysis will help determine what 

strategies and information sources experts use 

when making saccades across image pairs. 

Implications for training and practice 

It is likely too soon to draw firm 

conclusions about possible changes to training 

procedures given that we have only begun to 

explore various information theoretic 

approaches. However, we anticipate being 

able to make such conclusions in the coming 

years. 

B.7. Linguistic identifiers assigned 

to extracted features 

In addition to the visual-based 

approaches described in the previous sections, 

we collected linguistic labels from examiners 

in an attempt to use these to provide a source 

of data compression. 

To obtain linguistic labels, we presented 

examiners with 48x48 pixel crops from latent 

and inked prints that were centered on the 

fixations from expert examiners. These were 

not otherwise standardized or rotated. We 

asked the examiners to provide a description 

of the features in such a way that this might 

enable individualization. 

This study produced mixed results. The 

examiners expressed some frustration with 

this task, since we asked them to provide just 

2-4 words or phrases per crop. They 

discovered that this format was not nearly rich 

enough to capture the detail of the crop that 

would enable individualization. They used 

phrases such as: “core, short ridge, incipient 

ridge, bifurcation up, recurve, ending ridge, 

bend down, short near core, and incipient 

spur”. While these were accurate, they were 

analogous to describing the Mona Lisa as 

‘white female with dark hair’. 

 Despite the relative sparse nature of 

language for the description of uniqueness in 

fingerprints, there may be situations in which 

linguistic identifiers may play a role. For 

example, mentors may use linguistic terms for 

perceptual features during training procedures 

with novice examiners. This may enable the 

trainee to comprehend the classes of different 

sources of detail in prints. 

B.8. Summary of data analyses 

Patterns of eye movements reveal 

whether experts move their gaze to regions 

that are most informative as defined by 

different information metrics. The collection 

of such data allows us to validate candidate 

metrics and improve them where they tend to 

deviate from expert eye gaze. In essence we 

are reverse-engineering the human visual 

system in order to better engineer computerbased approaches to fingerprint quantification. 

Linking the gaze back to the physical features 

of friction ridge patches will illustrate the 

nature of expertise, and help improve 

machine-based analyses of latent prints. 

C. Conclusions and implications 

for policy and practice. 

Prior to this project, virtually nothing 

was known about the differences between 

experts and novices in terms of the 

information they choose to acquire, since no 

one had collected the eye tracking data. Once 

we established a reliable software toolkit that 

enabled robust data collection, we were able 

to use expert/novice comparisons to 

demonstrate a number of important 

conclusions: 

First, while the basic timing of eye 

fixations and saccades does not differ between 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 22 of 53 

the two groups, experts make smaller 

saccades, which suggest that they are looking 

at clusters of nearby features or combinations 

of features. Novices may be attempting to 

match individual features across the prints, 

which are not nearly as diagnostic since the 

combinatorial explosion of clusters makes 

these much more diagnostics. 

Second, experts acquire data from 

resource-rich regions of fingerprints, which 

are associated with more minutiae. However, 

they are also more willing to wander into 

regions that have poorer signal-to-noise ratios 

in an attempt to find information from a 

partial print that might prove diagnostic. We 

can infer this from the structure of the feature 

set found thorough the ICA data reduction 

procedures. 

Third, we documented the conditions 

under which experts may be more or less 

consistent with each other as a group, 

depending on image quality and the content of 

the fingerprints. 

Fourth, we adapted techniques from 

machine translation to demonstrate how these 

procedures could be used to identify 

corresponding regions of the fingerprints. 

Finally, we demonstrated how we could 

create several candidate information theoretic 

metrics that describe in quantitative terms the 

information contained in fingerprints. We 

used the expert/novice comparisons to 

demonstrate how one can discriminate 

between these candidate measures. 

These conclusions have a number of 

applied applications. First, they go a long way 

toward addressing the criticism that experts do 

not bring anything of value when they testify. 

It is clear now from a number of studies that 

experts have special abilities that exceed those 

of novices, and therefore should be allowed to 

share that expertise in court. 

The results also suggest that engineers 

who are refining fingerprint-matching 

algorithms should look to data from human 

experts to validate their approaches. The 

advantage of approaches such as the data 

reduction and information theoretic metrics 

described in Section B is that the models are 

built on the same math and computer code that 

computer-based fingerprint identification 

systems use. Therefore the results should be 

easy to adopt by those working on computerbased matching. 

All of our studies were done using 

abbreviated testing conditions in order to 

obtain the amount of data necessary to test 

candidate models, and therefore care should 

be taken when generalizing these results to 

full-blown latent print examinations. Future 

studies should address the changes that might 

occur when experts are given more time with 

the images. 

Finally, there is a wealth of other studies 

and analyses that could be done with the rich 

nature of eye gaze data, and additional 

scientists should be recruited to help address a 

variety of different questions related to the 

nature of expertise in latent print examiners. 

D. Products, publications and 

presentations 

D.1. Products 

This project produced several different 

scientific advancements. The most 

fundamental achievement was the 

demonstration that eyetracking data could be 

reliably gathered in the field from active latent 

print examiners. This took a great deal of 

work to accomplish because no system existed 

that was reliable and portable for such an 

endeavor. Our ExpertEyes software is 

available to download for free, and we have 

several groups interested in adopting our 

system. The hardware schematics are also 

online. 

The second major achievement is the 

demonstration that the eyetracking data 

acquired from experts and novices could be 

used to improve upon existing quantitative 

approaches to fingerprint detail. There are 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 23 of 53 

many different approaches, and we have 

endeavored to touch on each of these 

techniques. The best demonstration of the 

utility of our approach comes from the 

information theoretic techniques, in which the 

data from experts can be used to demonstrate 

which of several different computations of 

entropy are most likely to be used by experts. 

In addition to these accomplishments, we 

have the following publications and 

presentations that reflect our dissemination 

efforts. 

D.2. Publications 

The following publications are made 

possible with the support of the current grant. 

Busey, T.A. & Loftus, G. R. (2007). 

Cognitive science and the law. Trends 

in Cognitive Sciences, 11, 3, 111-117. 

Busey, T.A. & Dror, I. (in press). Special 

Abilities and Vulnerabilities in 

Forensic Expertise. To appear in The 

Latent Print Sourcebook. Alan 

McRoberts (Ed). Published by the 

National Institutes of Justice. 

Busey, T. A., Yu, C., Wyatte, D., 

Vanderkolk, J., Parada, F., & Akvipat, 

R. (Submitted Manuscript). 

Consistency and Variability Among 

Latent Print Examiners as Revealed by 

Eye Tracking Methodologies. Journal 

of Forensic Identification. 

Busey, T. A. & Parada, F. (in press). The 

Nature of Expertise in Fingerprint 

Examiners. In press at Psychonomic 

Bulletin & Review. 

D.3. Presentations 

We believe that outreach activities in the 

form of presentations at scientific and 

educational conferences is crucial to 

dissemination activities. We have made every 

effort to present our work for critical appraisal 

and discussion, including the following 22 

presentations, listed in chronological order: 

Busey, T. A. & Vanderkolk, J. R. (2005). 

Expertise in Fingerprint Examiners. 

Paper presented at the New England 

Society for Identification annual 

meeting. 

Busey, T. A. (2005). The role of 

configural processing in the 

development of visual expertise. Paper 

presented at the Annual 

Interdisciplinary Conference. 

Busey, T. A., & Vanderkolk, J. R. (2005). 

Behavioral and electrophysiological 

evidence for configural processing in 

fingerprint experts [Abstract]. Journal 

of Vision, 5(8), 635a, 

http://journalofvision.org/5/8/635/ 

Vanderkolk, J. R., & Busey, T. A. (2005). 

Forensic individualization of images. 

Forensic Identification Seminar of 

Toronto, Canada. 

Vanderkolk, J. R., & Busey, T. A. (2005). 

Expert and novice fingerprint studies. 

Fingerprint Society Lectures, Brighton, 

United Kingdom. 

Busey, T. A. & Vanderkolk, J. R. (2005). 

Expertise in Fingerprint Examiners. 

Paper presented at the International 

Association of Firearm and Toolmark 

Examiners annual meeting. 

Busey, T.A. (2006). How The Cognitive 

and Visual Sciences Might Help (and 

Hurt) in a Daubert Hearing. Paper 

presented at the 2006 meeting of 

Forensic Document Examiners 

Society. 

Busey, T.A. (2006). Measuring Expertise 

in Latent Print Examiners to Improve 

the Quantitative Analyses of Latent 

Prints. Paper presented at the 2006 

meeting of the International 

Association for Identification, Boston, 

MA. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 24 of 53 

Busey, T.A. (2006). Quantifying Human 

Expertise During Latent Print 

Examinations. Paper presented at the 

annual meeting of the National 

Institutes of Justice, Boston, MA. 

Schneider, B., DeLong, J., Wyatte, D., 

James, K., & Busey, T. (2007). The 

neural correlates of face-like expertise 

in fingerprint examiners [Abstract]. 

Journal of Vision, 7(9):575, 575a, 

http://journalofvision.org/7/9/575/, 

doi:10.1167/7.9.575. Paper presented 

at the Vision Sciences Conference, 

Sarasota, FL. 

Blaha, L., & Busey, T. (2007). 

Electrophysiological substrates of 

configural learning [Abstract]. Journal 

of Vision, 7(9):796, 796a, 

http://journalofvision.org/7/9/796/, 

doi:10.1167/7.9.796. Paper presented 

at the Vision Sciences Conference, 

Sarasota, FL. 

Busey, T. A. (2007) Expertise in Latent 

Print Examiners. Invited paper 

presented at the Indiana Society for 

Identification annual meeting, 

Evansville, IN. 

Busey, T.A. (2007). Measuring Expertise 

in Latent Print Examiners. Paper 

presented at the 2007 meeting of the 

International Association for 

Identification, San Diego, CA. 

Busey, T.A. (2007). Extracting Expertise 

from Latent Print Examiners. Paper 

presented at the annual meeting of the 

National Institutes of Justice, San 

Diego, CA. 

Busey, T. A. (2007) Cognitive psychology 

and fingerprint experts. Paper 

presented at the Illinois Society for 

Identification annual meeting, Peoria, 

IL. 

Busey, T., Schneider, B. & Wyatte, D. 

(2008). Expertise and the width of the 

visual filter in fingerprint examiners. 

Volume 8, Number 6, Abstract 178, 

Page 178a 

http://journalofvision.org/8/6/178/ 

Schneider, B., Harman-James, K., Wyatte, 

D & Busey, T. (2008). A noise x 

inversion paradigm reveals the nature 

of fingerprint expertise for latent print 

examiners in EEG and fMRI. Volume 

8, Number 6, Abstract 177, Page 177a 

http://journalofvision.org/8/6/177/ 

 Wyatte, D. & Busey, T. (2008). Low and 

high level changes in eye gaze 

behavior as a result of expertise. 

Volume 8, Number 6, Abstract 112, 

Page 11 

http://journalofvision.org/8/6/112/ 

Busey, T, & Vanderkolk, J. (2008). 

Measuring Expertise in Latent Print 

Examiners. Talk presented at the 2008 

International Association for 

Identification Annual Meeting in 

Louisville, KY, August 2008. 

Busey, T, Yu, C. & Vanderkolk, J. (2008). 

The Varieties of Expertise in Latent 

Print Examiners. Talk presented at the 

2008 International Association for 

Identification Annual Meeting in 

Louisville, KY, August 2008. 

Busey, T. (2008). Expertise in Latent Print 

Examiners As Revealed by Behavior, 

Electrophysiology, and Eyetracking. 

Invited Symposium presented at the 

2008 Psychonomics Society Annual 

Meeting, Chicago, IL. 

Busey, T, Yu, C., Akavipat, R. & 

Vanderkolk, J. (2009). Adding Human 

Expertise to the Quantitative Analysis 

of Fingerprints. Talk presented at the 

2009 National Institutes of Justice 

Grantees meeting in Tampa, FL, 

August 2009 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 25 of 53 

Appendix 

Methods for Latent/Inked comparisons 

The procedures below describe how we collected the data for our latent/inked comparisions. 

Stimuli 

The stimuli for Experiment 1 come from the National Institutes of Standards and 

Technology Special Database 27, which has previously determined identifications of latent and 

inked prints that are typical of what is traditionally found during casework. The images were 

grouped into good, bad and ugly prints by an expert with an obvious affinity for Clint Eastwood 

western movies. We used a mixture of all three types, which produced trials that could be quite 

challenging at times. 

We created three lists of images, each of which has 30 pairs of images. We had relatively 

few non-matching prints since our analyses are less applicable to non-matching pairs. List 1 had 

5 non-matches (exclusions), list 2 had 3 nonmatches, and list 3 had 8 nonmatches. 

Participants 

Our experts were recruited from forensic science laboratories in Indiana, Illinois and 

Nevada that were associated with state or large metropolitan agencies. They had an average of 

15.3 years working as latent print examiners and were an average of 45 years old. There were 4 

men and 2 women. Four of the six had trained other examiners. The novices were recruited from 

the Indiana University student body, had no prior experience with latent prints, and tended to be 

younger with a mean age of 23 years. There were three men and three women. 

All participants were tested according to the procedures of the Human Subjects Protection 

committee of Indiana University. 

Procedures 

Participants were seated approximately 36 inches away from a 17” inch LCD monitor set at 

a resolution of 1024x768 pixels. The images were scaled so that the latent and inked prints 

together filled the horizontal dimension, with 138 pixel horizontal borders on the top and bottom 

of the prints. The monitor was part of a model 1750 Tobii eyetracking system (Tobii 

Technology) which uses infrared cameras positioned on the monitor to track the position of the 

eye gaze by monitor both eyes. After a calibration procedure that established the relation 

between the observer’s eye position and positions on the screen, the participant was shown pairs 

of prints and asked to determine whether they came from the same source. They were given up to 

one minute to make this determination, and if they came to a conclusion sooner they stated this 

conclusion and proceeded to the next image. An experimenter recorded their response as either 

identification or exclusion. Due to a disk crash and a corrupted backup disk, the responses for 

three novices were lost. 

Methods for Inked/Inked comparisons 

The methods below describe how we collected data on inked/inked comparisons. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 26 of 53 

Stimuli 

We created 30 pairs of images by obtaining clean impressions from volunteers at Indiana 

University. These were made using ink stamp pads and placed on glossy ink jet photo paper. 

These were scanned using an Epson 4870 scanner at 1200 pixels per inch, and then cropped so 

that they fit inside a rectangle of 840x1050 pixels. Two image pairs were selected to be nonmatching. The non-matching prints were selected by left-right reflecting or image reversing a 

print from the same finger of the other hand. These often have the same general ridge flow but 

will differ in the exact ridge details. 

Participants 

We tested 6 expert and 7 novice participants, including one expert who had participated in 

Experiment 1. The experts were recruited at forensic identification conferences in Michigan, 

Illinois and Indiana, while the novices were members of the Indiana University community. The 

mean age of the experts was 43 and the mean number of years of experience was 9.3. There were 

5 men and 1 woman. All had self-reported 20/20 vision (corrected or uncorrected). 

The novices had a mean age of 32 years and there were 4 men and 3 women. 

Procedures 

Participants were seated approximately 60 cm (~24 inches) away from a 21” LCD monitor. 

To ensure the quality of the data we used a chinrest for some participants to reduce head 

movements. Participants wore a head-mounted eyetracker which uses two small cameras to 

monitor the eye and the view of the scene respectively according to the hardware proposed by 

(Babcock & Pelz, 2004). Both cameras are mounted and specially located on a pair of 

lightweight safety glasses. One infrared light is located next to the eye-camera in order to 

illuminate the eye properly. This light provides us a constant spot of white light known as the 

first corneal reflection, which will be used for further offline analysis using the ‘ExpertEyes 

Software’, an open source approach for analyzing eye-tracker data 

(http://code.google.com/p/experteyes/wiki/ExpertEyes) developed by our research group. 

Using this setup we record the streaming from both cameras, which is split later into image 

sequences. These images are used by the two modules of our software for further temporal 

alignment, calibration and gaze estimation. The first module uses the images from the eye stream 

in order to calculate the relationship in time between the pupil and the corneal reflection and fit 

the eye model. The second module uses the images from both streams and the eye model data to 

synchronize and calibrate both streams. 

The ExpertEyes eyetracking system allows the computation of the average error of the eye 

tracker. The Tobii system reports the average error of 0.5 degrees of visual angle under typical 

use. We found that our eye tracker produced values in a similar range. The mean error for 

experts was .71 degrees, while the mean error for novices was 0.60 degrees. These values were 

not significantly different for the two groups (t(11) = 2.02; p > 0.05). Thus we are confident that 

our eye tracking results from the inked/inked comparisions are comparable in accuracy to those 

of the commercial system used for the latent/inked comparisions. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 27 of 53 

Figures 

Figure 1. Example of a perceptual illusion that cannot be overcome through knowledge. 

The red lines are actually parallel, but knowledge of this does not make them appear straight. 

However, a sheet of paper next to the line illustrates that it is straight. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 28 of 53 

Figure 2. Our eye tracker worn by a participant in the eyetracking studies. It consists of one 

camera that monitors the position of the eye relative to the head, and a second camera that 

monitors the position of the head relative to the images being examined. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 29 of 53 

Figure 3. Example eye model fit which estimates the locations of the pupil (dark circle) and 

corneal reflection (white circle). The left panel shows the masked eye camera frame, while the 

right panel shows the fit of the eye model, which adjusts the locations of the black and white 

ovals to find the best overlap with the pupil and corneal reflection. The centers of each oval 

define the position of the eye.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 30 of 53 

Figure 4. Forward eye model interface. The pupil (dark circle) and corneal reflection (white 

circle) are identified using a forward eye model that adjusts the parameter settings based on the 

location of the pupil. Once a set of parameters is found for each region, the program fits the eye 

model to the entire dataset, which can take 2-16 hours of CPU time on a 3 GHz computer.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 31 of 53 

Figure 5. Eliminating image distortions. When the head-mounted video camera is positioned 

away from the center of the monitor, image distortions result. In this case the upper-right corner 

of the monitor in the image on the left is distorted. We created an undistortion algorithm that 

eliminates these distortions and allows us to accurately project the eye location onto the original 

image (right panel).

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 32 of 53 

Figure 6. Search algorithm to identify the corners of the 

monitor in the scene camera after barrel distortions have been 

removed from the image. Red squares indicate our search 

space and green marks indicate the identified corner regions.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 33 of 53 

Figure 7. Screen that plots the eye data, along with the fitted pupil and corneal reflection 

parameters, along with the estimated eye gaze location in the scene camera (red plus). Graphs 

along the bottom show the pupil location for frames around the current frame. The large 

discontinuities in the blue and red curves are eye blinks. This particular screen allows the user to 

identify and correct portions of the data that have missing or incorrect data.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 34 of 53 

Figure 8. Gaze data from one subject. Green dots are raw gaze estimates, red dots are 

fixations that are determined using a velocity-based measure, and blue lines are saccades from one 

location to another.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 35 of 53 

Figure 9. Results of the calibration procedures. Green dots are locations on the monitor that 

were fixated, and red dots are the estimated eye positions. The close clustering of each set of red 

dots illustrates the accuracy of our system, which is comparable to commercial systems. We 

typically see error on the projected monitor of less than 1 degree of visual angle and often less 

than .5 degrees, which is similar to commercial systems such as the Tobii system.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 36 of 53 

Figure 10. System integration screen which binds together folders containing scene 

information, eye information, gaze and corner information, and allows the user to synchronize 

the video streams, calibrate the gaze information, clean bad frames, and mark events from the 

experiment.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 37 of 53 

Expert Data Novice Data

Figure 11. Examples of heat maps. Color indicates the amount of time spent at each 

location. The left column contains data from a latent print expert on two pairs of images, while 

the right column contains data from a novice. In general, experts show much tighter 

concentrations of fixations around relatively few locations, while novices direct their attention to 

many more locations. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 38 of 53 

Figure 12. Graphical illustration of counting nearby minutiae. The Universal Latent 

Workstation was used to computer-code the locations of the minutiae, shown here as green 

crosses. We then determined the fixations for each user (shown as red crosses) and counted the 

number of minutiae contained in a circle centered on each fixation (green circles). 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 39 of 53 

Figure 13. Graphical illustration of fixations of all novices (in blue) and all experts (in red) 

for one representative image pair. The expert fixations tend to focus at and below the core, while 

novice fixation tend to distribute along the top of the fingerprint. The core tends to have many 

more minutiae, leading to a difference between the groups in terms of the number of nearby 

minutiae. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 40 of 53 

Figure 14. Parallel translation allows for the inference of correspondences between latent 

and inked prints.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 41 of 53 

Figure 15. Fixations (circles) are clustered based on spatial location and a distance-based 

criterion. Fixations that fall in the same cluster are colored using the same color. Machine 

translation algorithms can be used to assign correspondences between clusters in the two prints by 

assuming that experts are looking at corresponding locations one after another. The machine 

translation algorithm uses the temporal information in the gaze sequence to identify 

corresponding clusters. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 42 of 53 

Figure 16. Left panel: Results of the machine translation analysis demonstrating that experts 

had many more correspondences than novices. Right panel: Histogram of the number of 

correspondences for each of our 6 novices. There is an outliner (number 4) who turns out to be a 

graduate student working on the fingerprint project and therefore not a true novice.

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 43 of 53 

 

 Figure 17. Example image window patches extracted at eye fixation locations that can be 

used to induce the nature of the features inspected by examiners. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 44 of 53 

Figure 18. Feature basis set derived from expert data (left) and novice data (right). 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 45 of 53 

Figure 19. The process of converting fingerprint ridges to strings of direction orientation 

information and converting edges to strings of orientations. 

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been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 46 of 53 

Figure 20. Amount of information contained at each location, as determined by the 

information contained in relative angle measurements. The amount of red at each location is a 

visualization of how much information that location contains by this measure. This particular 

measurement looks at the orientation changes that occur along a ridge. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 47 of 53 

Figure 21. The information contained in the absolute orientation of ridge elements. Red 

regions are those that are more informative by this measure. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 48 of 53 

Figure 22. Fixation data from one expert overlaid on one information metric (relative 

information). Fixations tend to occur in higher-information regions. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 49 of 53 

Figure 23. A second expert on the same set of prints and information metric as Figure 22. 

Again we see the fixation from experts in higher-information regions. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 50 of 53 

Figure 24. Data from a novice on the same set of prints and information metric as Figure 22 

and Figure 23. The fixations appear to land in lower-information regions. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 51 of 53 

Figure 25. Similar to Figure 24, this is data from a second novice with fixations that tend to 

land in lower-information regions. 

This document is a research report submitted to the U.S. Department of Justice. This report has not 

been published by the Department. Opinions or points of view expressed are those of the author(s) 

and do not necessarily reflect the official position or policies of the U.S. Department of Justice.
Final Report #2005-MU-BX-K076 Busey, T., PI 

Page 52 of 53 

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This document is a research report submitted to the U.S. Department of Justice. This report has not 

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This document is a research report submitted to the U.S. Department of Justice. This report has not 

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and do not necessarily reflect the official position or policies of the U.S. Department of Justice.