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

Department of Justice to prepare the following resource: 

Document Title: ACEware Latent Fingerprint Identification 

Research and Software Development 

Author(s): William J. Chapman, R. Austin Hicklin 

Document Number: 251092 

Date Received: August 2017 

Award Number: 2013-R2-CX-K011 

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

Justice. This resource is being made publically available through the 

Office of Justice Programs’ National Criminal Justice Reference 

Service. 

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. 
ACEware Latent Fingerprint Identification

Research and Software Development

Final Technical Report

Funded by the National Institute of Justice 

Award Number: 2013-R2-CX-K011 

William J. Chapman 

R. Austin Hicklin 

Noblis 

28 February 2017 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

2

Abstract 

There is currently no widely-used standard method of detailed documentation of the latent 

print examination process: how or whether examiners annotate what they use as the basis for 

their conclusions varies widely among agencies, and when examiners do document their work, 

there is extensive interexaminer variation because there is no uniform training in feature-level 

documentation. There is a need for greater standardization of documentation through more 

rigorous and consistent training, and through tools for operational casework. ACEware seeks 

to address that problem by providing a platform for standards-based detailed annotation of 

the latent print examination process. ACEware is an innovative software tool for use in training 

new latent print examiners in standard, reproducible documentation of examination — as well 

as for use by experienced case-working latent print examiners in documenting actual casework. 

ACEware builds upon the Universal Latent Workstation (ULW), which allows users to create, 

edit, view, and manage latent fingerprint transactions. ACEware extends ULW by providing 

functionality that supports both instructor-led and self-led training, extending its functionality 

for non-AFIS casework, and providing the capability to create standardized data sets for 

research and training. Because ACEware documentation is based on an international standard 

— the ANSI/NIST Extended Feature Set — detailed documentation of a complex latent print 

examination can be exchanged with other examiners, or archived for future review. ACEware 

is being developed by Noblis in collaboration and consultation with several Federal, state, and 

local law enforcement partners. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

3

Table of Contents 

Executive Summary..............................................................................................................................................5

1 Introduction .....................................................................................................................................................1

1.1 ACE-V ..................................................................................................................................................................1

1.2 The lack of standardized documentation ...........................................................................................1

1.3 ANSI/NIST-ITL and the Extended Feature Set..................................................................................4

1.4 Moving toward probability models .......................................................................................................5

1.5 Universal Latent Workstation (ULW) and ACEware .....................................................................5

2 ACEware functionality ................................................................................................................................. 6

2.1 Training .............................................................................................................................................................6

2.2 Datasets ..........................................................................................................................................................15

2.3 Comparing and combining minutia markup ..................................................................................18

2.4 Enhancements to enable non-AFIS casework ................................................................................24

3 Dissemination .............................................................................................................................................. 32

4 Conclusions ................................................................................................................................................... 33

4.1 User feedback ...............................................................................................................................................33

4.2 Impact .............................................................................................................................................................34

4.3 Implications ..................................................................................................................................................34

5 References ..................................................................................................................................................... 36

6 Appendix ........................................................................................................................................................ 39

6.1 Appendix A: Data Set Directory Specification ................................................................................39

6.2 Appendix B: Compliments and Enhancement Suggestions .......................................................46

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

4

Table of Figures 

Figure 1 – Example of Groups in Transaction Manager ......................................................................... 6

Figure 2 - Multiple group assignments ......................................................................................................... 7

Figure 3 - New Lesson form .............................................................................................................................. 7

Figure 4 - Populated Session form ................................................................................................................. 8

Figure 5 - Answer files linked to Lesson content ...................................................................................... 8

Figure 6 - Examiner Training dashboard ..................................................................................................... 9

Figure 7 - Completion of assigned task prompt ........................................................................................ 9

Figure 8 - Student and answer markup overlay..................................................................................... 10

Figure 9 - Instructor dashboard ................................................................................................................... 11

Figure 10 - View by Student mode .............................................................................................................. 12

Figure 11 - View by Data File mode ............................................................................................................ 13

Figure 12 - Multiple students selected in View by Data File mode ................................................. 13

Figure 13 - Multiple student’s markup, including answer file, on a single image..................... 14

Figure 14 - Self-led training in Latent Editor .......................................................................................... 15

Figure 15 - Data set import options ............................................................................................................ 16

Figure 16 - Data browser containing multiple data sets .................................................................... 17

Figure 17 - Single data set relationship tooltip ...................................................................................... 17

Figure 18 - Multiple data set relationships tooltip ............................................................................... 17

Figure 19 - Five sets of markup viewed as an overlay on a single latent image ........................ 20

Figure 20 - Clustering options....................................................................................................................... 21

Figure 21 - Voted markup results only ...................................................................................................... 22

Figure 22 - Voted markup results with additional markup ............................................................... 23

Figure 23 - Markup colored by consensus ............................................................................................... 24

Figure 24 - Verification configuration........................................................................................................ 25

Figure 25 - Verifications count ..................................................................................................................... 25

Figure 26 - Assigning verifications .............................................................................................................. 26

Figure 27 - Verification task .......................................................................................................................... 26

Figure 28 - Completed verification statuses ............................................................................................ 27

Figure 29 - Enter comment option in Comparison Tool ..................................................................... 28

Figure 30 - Commenting on individual features .................................................................................... 28

Figure 31 - Viewing a feature-level comment ......................................................................................... 28

Figure 32 - Reports tab in Transaction Manager ................................................................................... 29

Figure 33 - Quality Assurance Report example ...................................................................................... 30

Figure 34 - Markup Summary Report example ...................................................................................... 31

Figure 35 - Blank EFS Fields in Comparison Tool ................................................................................. 32

Figure 36 - Graphical View of Data Set Directory Hierarchy ............................................................ 40

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

5

Executive Summary 

There is currently no widely-used standard method of detailed documentation of the latent 

print examination process: how or whether examiners annotate what they use as the basis 

for their conclusions varies widely among agencies, and when examiners do document their 

work, there is extensive interexaminer variation because there is no uniform training in 

feature-level documentation. There is a clear need to improve and standardize the 

documentation of casework: a need for examiners to be able to unambiguously and 

consistently document the features they use to justify their decisions. The purpose of 

ACEware is to fill that need by providing a software platform for standards-based detailed 

annotation of the latent print examination process — for use in training new latent print 

examiners in standard, reproducible documentation of examination, as well as for use by 

experienced case-working latent print examiners in documenting actual casework. 

The ACEware project developed software that provides functionality to support the detailed 

documentation of latent print examination. ACEware also provides functionality to facilitate 

training and develop datasets for research or training, as well as functionality for 

documentation of operational casework. 

The ACEware software application builds upon the pre-existing Universal Latent 

Workstation (ULW) application provided by the Federal Bureau of Investigation (FBI) 

Criminal Justice Information Services (CJIS) and developed by Noblis.1 ULW provides latent 

print examiners with tools to mark-up latent print images; submit latent print searches to 

local, state, and federal Automated Fingerprint Identification Systems (AFIS) including the 

Next Generation Identification (NGI) system; perform comparisons of latent prints and 

returned candidate images; and provides latent print case management tools that store and 

sort transactions related to operational casework. By building on ULW, ACEware is able to 

take advantage of the extensive capabilities of the ULW platform, and the ACEware 

functionality can be freely distributed to law enforcement agencies as part of ULW. 

ACEware introduces new capabilities to ULW for use in a variety of situations including 

casework, training, and research. These capabilities include: 

1. Data set based, instructor led training and data set or file based, self-led training: 

o Instructor led training typically consists of Lessons composed of individual 

Sessions. Lessons contain the files to be used for training, based on selected data 

sets, and cover the overall theme or concept to be taught. Each Lesson contains 

one or more Sessions in which student and instructor users are identified by 

Groups created and managed by administrative users. Each session within a 

lesson can contain the same student and instructor users, similar to a class that 

meets multiple times a week, or completely different groups can be identified for 

each role across different sessions within a single lesson. Sessions may also 

contain appropriate answer files that are linked to the individual files within a 

lesson to be used as a basis for automatic grading of student work upon 

completion. Users in a session’s “student” role have access to an examiner training 

 

1 ULW was designed by Tom Hopper (FBI CJIS) and Austin Hicklin (Noblis, then named Mitretek) 

starting in 1998, and has been developed and maintained by Noblis ever since then, under contract to 

the FBI. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

6

dashboard that displays the work to be completed, the student’s individual 

progress on a lesson, and any metrics that may exist as a result of grading against 

an available answer file. Students are able to open each file within a lesson to 

perform markup within the appropriate ULW application, and subsequently 

check-in their work upon completion. Students also have the option of viewing

their own markup and the markup contained within an answer file as an overlay 

on a single image to better understand how their work compares to the answer 

after grading has been performed. Users in a session’s “instructor” role have 

access to their own instructor dashboard allowing them to see the progress the 

class as a whole, the progress of each individual student within a class, as well as 

any metrics that are generated as a result of “grading” a student’s work against 

the appropriate answer file, if available. Instructor users also have the ability to 

view multiple student’s markup on a single image for the purposes of discussion 

and developing an understanding of consensus levels among students. 

o Self-led training can be accomplished in a manner similar to instructor led 

training where a user may play the role of both student and instructor for a data 

set based lesson. Users also have the option of selecting one or more markup files 

to load as an overlay on top of their own markup within the Latent Editor 

application, allowing an individual to compare themselves against ground truth 

data or other examiners in the field at any time. This provides examiners with the 

same comparison functionality available to instructor users and provides a means 

for individual examiners to test themselves outside of structured training 

scenarios. 

2. Creation and management of datasets for use in research or training, including the ability 

to set relationships between all files contained within the dataset. The intent is to 

propose a standard specification for friction ridge datasets that can be used in the 

dissemination of future datasets. 

3. Comparison or consolidation of the markup of a single latent print by multiple examiners. 

Comparison of markup can be used to facilitate discussion in training scenarios, to 

analyze key differences between examiner mark-up (interexaminer variation) and 

identify potential red flags or areas of improvement, and to compare against ground truth 

data. Combination of markup can be used to develop “ideal” markup for training datasets, 

or to develop consensus among multiple examiners. 

4. A variety of enhancements to ULW to enable non-AFIS casework, including: 

o The ability to perform blind or non-blind verifications from within the ULW software 

application. In a blind verification, the original examiner’s markup and conclusion are 

removed and the verifying examiner is given only the latent print and a single 

candidate exemplar; in a non-blind verification, the verifying examiner is given a copy 

of the original examiners’ work, including all marked features and conclusion, in 

order to review the work performed. In the event both examiners come to the same 

conclusion, the comparison has been successfully verified and is marked as such 

within the software. If the examiners come to conflicting conclusions, a conflict will 

be registered in the software as flag for administrative users to begin a conflict 

resolution process. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

7

o Individual feature annotations. Feature-level comments allow examiners to record 

any pertinent information about a particular feature marked during the analysis, 

comparison, or verification stages of the examination process. This provides a 

method by which examiners may communicate why or how a particular feature was 

marked and can be used by verifying examiners in a non-blind verification process, 

review boards, and may even serve as a basis for discussion within a court room 

presentation. 

o Basic QA reporting capabilities. Quality Assurance reports may be generated to flag 

files for “review” and “rigorous review” based on analysis markup and retained 

markup between analysis and comparison stages of the examination process. Markup 

Summary reports display a distribution of features with respect to the examiners’ 

assessments of value and comparison conclusions. Both of these reports were 

designed to help ensure sufficient levels of detailed analysis are being performed and 

to pinpoint areas in need of improvement. This reporting functionality also serves to 

demonstrate the types of automated QA made possible by ACEware. 

o Expansion of Extended Feature Set (EFS) data field support, specifically within the 

ULW Comparison Tool. EFS fields such as Method of Feature Detection, Latent 

Processing Method, Evidence of Fraud, Latent Substrate, and Latent Matrix were 

previously only available during the analysis stages within ULW Latent Editor. With 

ACEware, examiners are able to enter and modify EFS field data and save such entries 

to COMP files for exchange between examiners, organizations, and for record keeping. 

The project involved collaboration with the law enforcement agencies that were our official 

partners (Fairfax County Police Department, New York State Division of Criminal Justice 

Services, and Arizona Department of Public Safety Crime Laboratory), as well as additional 

testers (FBI Laboratory, Royal Canadian Mounted Police, and Cuyahoga County Regional 

Forensic Science Laboratory). An Alpha test version was released for feedback in January 

2015. Beta test versions were released in March 2015, November 2016, and early December 

2016. 

The reactions to the ACEware functionality suggest the latent print community is indeed 

looking for and open to solutions for organizing casework in a consistent manner, training 

examiners on proper markup methods, and electronic verification methods. Many have 

stated a desire and need for features such as data set creation and management (not just for 

training but for casework purposes as well), training, and verifications. 

Beyond the specific functionality implemented in ACEware, adoption of ACEware is 

intended to improve the rigor, transparency, standardization, and quantifiability of the 

latent print examination process by: 

 Providing a standardized approach for applying the ACE-V method, building upon 

SWGFAST standards and EFS 

 Facilitating increased consistency and proficiency in feature selection and detailed 

documentation by latent print examiners 

 Improving the resolution of disagreements between examiners and verifiers (conflict 

resolution) 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

8

 Standardizing formats for data exchange, long-term data archiving, and evidentiary 

presentations 

 Providing a standard platform for collection of data and performance metrics 

 Providing a basis for quantitative analyses of latent print examination, including 

automated metric-based quality assurance, and quantitative examiner performance 

evaluations 

After the release of ACEware and completion of the development phase, future research 

can evaluate its effectiveness, and develop additional quantitative analyses of latent print 

examination. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

1

1 Introduction 

There is currently no widely-used standard method of detailed documentation of the latent 

print examination process: how or whether examiners annotate what they use as the basis 

for their conclusions varies widely among agencies, and when examiners do document their 

work, there is extensive interexaminer variation because there is no uniform training in 

feature-level documentation. There is a clear need to improve and standardize the 

documentation of casework: a need for examiners to be able to unambiguously and 

consistently document the features they use to justify their decisions. The purpose of 

ACEware is to fill that need. ACEware has been developed to address that problem by 

providing a software platform for standards-based detailed annotation of the latent print 

examination process — for use in training new latent print examiners in standard, 

reproducible documentation of examination, as well as for use by experienced case-working 

latent print examiners in documenting actual casework. 

Over the past ten to fifteen years — in response to Daubert challenges, 2 the Mayfield 

misidentification, criticisms of latent print examination, 3 the National Research Council’s 

report on forensic science,4 and now the PCAST report on forensic science5 — the latent 

print examination discipline has been undergoing a sea-change, reevaluating its basic tenets 

with the goal of improving the scientific basis for latent print examination.6 ACEware was 

developed to be part of that process: to put software tools in the hands of examiners in order 

to improve the rigor of the latent print examination process. 

1.1 ACE-V 

Analysis, Comparison, Evaluation, and Verification (ACE-V) is the prevailing framework used 

to guide a latent print examiner through the analytical decision making process. In the 

Analysis phase, the examiner assesses the quality and quantity of features in a finger-or 

palmprint and determines whether the print is of value for further examination. In the 

Comparison phase, the features are compared with the features observed on a known 

(exemplar) print to determine corresponding features or discrepancies. In the Evaluation 

phase, the examiner determines whether there is sufficient information to make an 

individualization (identification) or an exclusion. The Verification phase of ACE-V refers to 

an independent reevaluation of the work performed during ACE. 

1.2 The lack of standardized documentation 

While ACE-V requires detailed evaluation of often complex data, this information is not 

generally documented in a rigorous, quantifiable, and repeatable manner. The Scientific 

Working Group on Friction Ridge Analysis, Study and Technology’s (SWGFAST’s) Standard 

 

2 Starting with US v. Mitchell in 1999 [Mitchell99], there have been dozens of challenges to the 

admissibility of fingerprint evidence. 

3 e.g. [Cole05,Cole06,Dror06a,Dror06b,Haber08,Haber09,Mnookin08] 

4 [NRC09] 

5 [PCAST16] 

6 Note that this report was written simultaneously with Mr. Hicklin’s PhD thesis [Hicklin16]; some 

content, especially in the introduction, is shared without further attribution. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

2

for the Documentation of Analysis, Comparison, Evaluation, and Verification (ACE-V)7 defines 

the types of information to be documented during routine and complex examinations, but 

does not attempt to make detailed requirements regarding how features are to be selected 

or to standardize formats for documentation.8 Although SWGFAST directs examiners to 

document examination “such that another qualified examiner can determine what was done 

and interpret the data,” frequently Analysis and Comparison 9 assessments are not 

documented in sufficient detail for another qualified latent print examiner to understand 

what information the examiner used in making determinations.10 The details of how to 

document Analysis and Comparison are mostly unspecified, and SWGFAST’s standards are 

unenforced, leaving the details to be sorted out by agency standard operating procedures or 

by the examiners’ judgments. This practice can impair data integrity, increase susceptibility 

to bias, and compromise the sequential aspect of ACE-V, resulting in circular reasoning. The 

lack of standard procedures and underutilization of existing standard data formats (see 

Section 1.3) limits interchanges among examiners, long-term archiving of casework, 

validation, evaluation of examiner capabilities, quantitative analysis, and quality assurance. 

The Department of Justice Inspector General’s review of the Brandon Mayfield 

misidentification11 and the National Research Council report on forensic science12 were both 

critical of the lack of documentation of the latent print examination process. The Mayfield 

OIG report described how the lack of documentation contributes to circular reasoning,13 and 

indicated that required documentation may result in more reliable conclusions. 14 The 

Mayfield OIG report recommends more rigorous documentation (Recommendation 10) and 

separate documentation of features observed in the latent during the Analysis phase 

(Recommendation 11). The NRC report also called for required documentation, for 

transparency and to “provide the courts with additional information on which to assess the 

reliability of the method for a specific case.”

15

Subsequent research has shown that examiners’ value determinations and conclusions are 

not always reproduced by other examiners, and are not always even repeated by the same 

 

7 [SWGFAST-Doc12] 

8 The Scientific Working Groups (SWGs) have been replaced by the NIST-sponsored Organization of 

Scientific Area Committees (OSAC), but the existing SWG documents remain in force until OSAC 

replacement standards are formalized. 

9 Analysis and Comparison are capitalized when referring to the Analysis and Comparison phases of 

ACE-V.

10 Based on discussions with a variety of examiners. 

11 [Mayfield06] 

12 [NRC09] 

13 [Mayfield06], p191-192 

14 “The absence of substantive documentation requirements is a conspicuous shortcoming of the 

current SOPs. We believe that there is a strong possibility that if the examiner and verifier had been 

required to document the analysis and comparison phases of their examinations, they might have 

noticed more dissimilarities and appreciated the cumulative impact of them before reaching their 

flawed conclusions. [...] We believe that documentation would have facilitated a more objective 

comparison and evaluation, regardless of the particular standard utilized to declare an identification.” 

[Mayfield06], p202 

15 [NRC09] p 5-13

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ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

3

examiners.16 Such issues with reliability underscore the need to have clear and precise 

methods of documenting ACE-V. The recent FBI Laboratory/Noblis study Interexaminer 

variation of minutia markup on latent fingerprints17 detailed the extent to which examiners 

differ in the minutiae they see and use as the basis for their decisions; this can be seen in 

large part as the result of the lack of standardized documentation in examiner training and 

operational casework. 

The recent report on forensic science by PCAST underscored the importance of converting 

“latent print analysis from a subjective method to an objective method.”

18 Although 

automation of much of the latent print comparison process may be desirable, that would be 

a very long-term goal; in the meantime, it is reasonable to take steps to improve the rigor of 

latent print examination, by making it more transparent, standardized, and quantifiable. One 

step in that direction would be to require standardized documentation, in training and in 

casework. The absence of detailed documentation indicating which features were used in 

making a given decision is problematic due to a lack of transparency: without detailed 

documentation the actions and inner workings of the decision process are neither visible nor 

accessible, and are not fully articulated for any consumer of an examiner’s determinations 

(e.g. supervisors, other examiners, attorneys, judges). One effect of this has to do with the 

potential for bias: when comparing highly similar prints, the examiner runs the risk of 

confirmation bias or circular reasoning, in which additional features may be suggested 

during comparison based on the similarity. In the Mayfield misidentification, the initial 

examiner reinterpreted five of the original seven Analysis points to be more consistent with 

the (incorrect) exemplar.19 Without separately documenting the features observed in the 

analysis and comparison phases, the examiner may not realize — and the consumers of the 

decision cannot know — whether such bias affected the decision. 

In the past, detailed documentation of Analysis was often limited to that required for 

searches of Automated Fingerprint Identification Systems (AFIS), with proprietary 

instructions on which features to mark varying substantially by vendor. Other than for AFIS 

searches, most agencies do not require detailed markup to document the features of a latent 

in Analysis, nor corresponding features used in Comparison. Those agencies that do require 

markup vary substantially on how that markup is affected. Some agencies (even now) 

document minutiae using pinpricks in physical photographs. Some agencies annotate using 

general-purpose tools such as Adobe Photoshop: while this is reasonable at a small scale, 

such a generic format retains none of the semantics associated with the features; while 

another examiner, agency, or automated analysis tool could open and view the resulting file, 

the exact intentions of the original examiner would frequently be lost. More rigorous 

approaches have been implemented in the University of Lausanne’s PiAnoS (Picture 

Annotation System) and Latentworks by Mideo Systems.20 A limitation of both PiAnoS and 

Mideo Latentworks is that they are not (at least currently) standards-based systems and 

therefore detailed annotations in either cannot readily be exchanged with other systems, 

 

16 [Ulery11,Ulery12,Dror11,Langenburg09] 

17 [Ulery16] 

18 [PCAST16] p103 

19 [Mayfield06], p7 

20 [PiAnoS, MIDEO] 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

4

have no interoperability with AFIS workstations, and cannot be relied upon for long-term 

archiving. 

If detailed documentation were consistently used in casework, a variety of automated quality 

assurance tools could be used that would not otherwise be practical. For example, automated 

flagging of identifications based on suspiciously few features is only possible if such features 

are marked; automated flagging of examinations with extensive changes between Analysis 

and Comparison is only possible if the Analysis and Comparison features are always marked. 

Quality assurance procedures can take special steps (such as extra verification) for decisions 

that appear to be high risk — but only if the features used to justify the decision are 

documented in a machine-readable format. 

1.3 ANSI/NIST-ITL and the Extended Feature Set 

The Data Format for the Interchange of Fingerprint, Facial, & Other Biometric Information

(ANSI/NIST-ITL) is the most recent revision of a series of standards that began in the 1980s. 

These ANSI/NIST standards have been extensively used as the primary method of 

communicating biometric information for law enforcement and other large-scale 

identification purposes. The ANSI/NIST ITL standards are the basis for biometric and 

forensic specifications used around the world, including the FBI EBTS, DOD EBTS, DHS IXM, 

Interpol's INT-I, and a wide variety of national, state, and local application profiles.21

Based on recommendations from SWGFAST, in 2005 NIST chartered the Committee to Define 

an Extended Fingerprint Feature Set (CDEFFS) to define a standard definition of friction 

ridge features. The committee included 47 representatives from various US Federal 

Agencies, SWGFAST, the US and international latent fingerprint community, and engineers 

from a variety of AFIS vendors. The result was the Extended Feature Set (EFS), which was 

formally incorporated into ANSI/NIST-ITL in November 2011. 

The purpose of EFS is to define a quantifiable, standard method of characterizing the content 

of a fingerprint or other friction ridge image. EFS was designed to enable AFIS searches and 

responses, documentation of ACE-V casework (for archiving, interchanges among 

examiners, and courtroom presentations), review of examiners’ work (for quality assurance, 

validation and technical review, and conflict resolution), automated quality assurance, and 

quantitative analysis for research. 

Though EFS is now broadly used as a non-proprietary format for AFIS searches, it is not yet 

frequently used for markup of non-AFIS casework. To assist in the adoption of EFS by latent 

examiners, Markup Instructions for Extended Friction Ridge Features22 was released as a NIST 

Special Publication, providing instructions to examiners in marking friction ridge 

impressions in accordance with EFS to maximize consistency among examiners. The 

Extended Feature Set Training Tool23 implements the EFS Markup Instructions in free, webbased software. The Latent Interoperability Transmission Specification24 defines standard 

transactions for archiving and exchange of casework, to provide a basis for long-term 

 

21 [NIST-AppProfiles] 

22 [Chapman13b] 

23 [EFSTT] 

24 [Chapman13a] 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

5

standardization of how latent print examinations are documented, exchanged, verified, 

reported in legal contexts, archived, and made available for quantitative quality assurance. 

1.4 Moving toward probability models 

A great deal of ongoing research is being conducted on statistical models designed to 

quantify the probability that a latent print came from a specified source. This work builds on 

decades of work on the measurement of fingerprint individuality, 25 statistics supporting 

fingerprint examination,26 and AFIS algorithms.27 The intent of these efforts would be for the 

examiners’ decisions to be augmented or replaced with an estimate of the probability that 

two prints came from the same source. 

An ironic implication of many of the current models is that they are attempting to replace 

the examiners’ conclusions, but are increasing the reliance on the examiners’ feature 

markup: the models generally do not extract features themselves, but use examiner markup 

as input. Since feature markup is not standardized, probabilistic models that do not 

automatically extract features are highly sensitive to the interexaminer variability of feature 

markup. For these reasons, probabilistic models are dependent on the standardization of 

feature markup. Probabilistic models may well be the wave of the future for latent print 

examination — but they are only going to be effective if the latent print discipline 

standardizes the training and operational use of feature markup. 

1.5 Universal Latent Workstation (ULW) and ACEware 

The FBI’s Universal Latent Workstation (ULW) was developed starting in 1998 specifically 

as a tool for AFIS searching.28 ULW provides latent print examiners with tools to markup 

latent print images; submit latent print searches to local, state, and federal Automated 

Fingerprint Identification Systems (AFIS) including the Next Generation Identification (NGI) 

system; perform comparisons of latent prints and returned candidate images; and provides 

latent print case management tools that store and sort transactions related to operational 

casework. ULW serves as a reference implementation of EFS. ULW has been used extensively 

for years to conduct AFIS searches, to great success. 

ACEware extends ULW by providing training functionality, extending its functionality for 

non-AFIS casework, and providing the capability to create standardized data sets for 

research and training. By building on ULW, ACEware is able to take advantage of the 

extensive capabilities of the ULW platform, and the ACEware functionality can be freely

distributed to law enforcement agencies as part of a standard ULW release. 

Note the name “ACEware” is limited to the NIJ project: the software will be released as “ULW 

with ACE-V Enhancements.”

 

25 Survey in [Stoney01] 

26 Survey in [Neumann13a] 

27 [Wayman05, Maltoni09] 

28 ULW was designed by Tom Hopper (FBI CJIS) and Austin Hicklin (Noblis, then named Mitretek) 

starting in 1998, and has been developed and maintained by Noblis ever since then, under contract to 

the FBI. 

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6

2 ACEware functionality 

ACEware introduces new capabilities for use in a variety of situations including casework, 

training, and research. These capabilities include: 

 The ability to facilitate instructor or self-led training 

 Creation and management of data sets for use in research or training 

 Comparing and combining minutiae markup by multiple examiners 

 A variety of enhancements to ULW to enable non-AFIS casework 

2.1 Training 

In an effort to improve the level of detailed documentation performed by latent print 

examiners, as well as improve the quality of such documentation, ACEware was developed 

to facilitate classroom tutorials, self-training, and peer evaluation. By leveraging preexisting tools within the ULW suite, training can be facilitated using the same tools that are 

available for operational casework allowing student examiners to immediately apply 

lessons learned. At the center of ACEware training is the ULW Transaction Manager, with 

ULW Latent Editor and ULW Comparison Tool serving as tools for completing coursework 

as well as tools for facilitating discussions within a classroom setting. 

2.1.1 Instructor-led Training 

Instructor-led training focuses on using data sets to create and run Lessons for use in 

classroom training with multiple student examiners. When Instructor-led training is 

required, administrative users create Groups of users within the Transaction Manager 

application (see Figure 1 below) 

Figure 1 – Example of Groups in Transaction Manager 

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These groups are mainly used to identify individuals assuming the role of Student and 

Instructor users for training but may also be used for grouping users as a means of 

identifying their roles within the application (e.g., Admins, Supervisors, Examiners, etc.); 

individual users may be assigned to multiple groups allowing administrators the freedom 

to create any number of groups for a variety of purposes, extending this Group concept to 

areas other than Training. Figure 2 shows an example of a single user with multiple group 

assignments. 

Figure 2 - Multiple group assignments 

After creating a minimum of two groups, one for instructors and one for students, users 

may begin creating Lessons. 

When creating a Lesson, a user will identify a Lesson Name and provide any relevant 

details about that lesson in a Lesson Description. 

Figure 3 - New Lesson form 

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Users will then select a data set or multiple data sets that have either been created using 

the data set creation and management functionality introduced by ACEware, or by 

importing a data set that is formatted in accordance with the dataset specification. The 

content of the data set will then become the content of the Lesson. Figure 3 shows a new 

Lesson form with basic information populated in each field and a data set selection. With 

basic information entered and a data set selected, the Lesson can be saved. A Lesson cannot 

be used for training until individual Sessions are created within that Lesson. Where a 

Lesson contains the content to be presented during training, a Session identifies the users 

and their roles for a single event in which the content of a Lesson will be presented. 

Figure 4 - Populated Session form 

Figure 4 demonstrates a Session “form” with the various options that need to be defined. 

Users will enter any pertinent comments they feel are important to note for the particular 

Session, select the Groups containing users that will assume the role of Students and 

Instructors for that session, the date scheduled, number of days to complete the Lesson 

content, and whether or not that Session is accessible by students. Session creation also 

provides users with the ability to link an answer file to the Lesson content as shown in 

Figure 5. Answer files are identified per Session rather than Lesson, allowing flexibility in 

what students will be graded against as training progresses. 

Figure 5 - Answer files linked to Lesson content 

Answers that are linked prior to the completion of Lesson content will cause student work 

to be graded immediately once that work is committed. Answers that are linked after a 

student has committed work will cause committed work to be graded as soon as the 

answer file is linked. In either case, so long as an answer file is available for a given Lesson 

file, work will be graded automatically. With a Lesson created and one or more Sessions 

created within a Lesson, students and instructors will now have access to their respective 

dashboards. 

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Figure 6 - Examiner Training dashboard 

The student dashboard, or Examiner Training dashboard as seen in Figure 6, consists of 

two panels: the upper panel contains the Lessons in which the student is a part of while the 

lower panel consists of the content of a selected Lesson. By clicking the checkbox next to a 

Lesson name in the upper panel, the lower panel will display the content of the Lesson, the 

current status of the Lesson file, and any grading metrics that may be available. Doubleclicking a row in the lower panel will open the selected file in the appropriate ULW 

application in order for a student to complete their work; image files and search files will 

automatically open in ULW Latent Editor whereas COMP files will open in ULW 

Comparison Tool. Once a student completes their work and closes the ULW application, 

they will be asked if they would like to save their work. After saving, or not saving, the 

student is asked if they have completed the assigned task for the file. 

Figure 7 - Completion of assigned task prompt 

If a student would prefer to continue working on the file later, they may choose to not 

commit their work – this will set the status of the file to “In Progress” and allow them to 

return to it later. On the other hand, committing work sets the file status to “Complete” and 

students will be unable to make further changes. Instructor users have the ability to 

override statuses in the event a student accidentally commits their work. Committed work 

will be graded if an answer file is available. If the student work was image or search file 

based, the student will receive a “score” in the form of Matched, Missed, and False Minutia 

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values. These values are based on the clustering algorithm described in Section 2.3 and can 

be described as: 

o Matched minutiae are features marked in both the student and answer files that 

are found to exist within the same cluster 

o Missed minutiae are features marked in the answer file but not within the student 

file resulting in clusters containing a features present only in the answer file 

o False minutiae are features marked in the student file but not within the answer 

file resulting in clusters containing features present only in the student file 

Files that have been scored in this way can be opened again by the student examiner by 

double-clicking on the appropriate row. Opening a graded file will display both the 

examiners markup as well as the markup contained within the associated answer file as an 

overlay on a single image, as seen in Figure 8. 

Figure 8 - Student and answer markup overlay 

If a student performed work on a COMP file and an answer file is available, the student will 

see a listing of multiple data points (as opposed to Matched, Missed, and False values) 

including:

o Student Latent Points – number of points marked on the latent print image by the 

student 

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o Student Corresponding Pairs – number of corresponding pairs marked by the 

student 

o Expert Latent Points - number of points marked on the latent print image in the 

answer file

o Expert Corresponding Pairs – number of corresponding pairs marked in the 

answer file

o Student Determination(s) – the determination(s) marked by the student

o Expert Determination(s) – the determination(s) marked in the answer file

o Student Latent Value – the Assessment of Value marked by the student for the 

latent print image

o Expert Latent Value – the Assessment of Value marked in the answer file for the 

latent print image

o Student Exemplar Values – the Assessment of Value marked by the student for 

each exemplar image within the file

o Expert Exemplar Values – the Assessment of Value marked in the answer file for 

each exemplar image within the file

Student examiners may continue to work on Lesson files until all work for a particular 

Lesson has been completed or are otherwise instructed to stop. 

Figure 9 - Instructor dashboard 

The Instructor dashboard, shown in Figure 9, is similar to the Examiner Training 

dashboard with some key differences. Instructors will see a list of the Lessons they are 

facilitating, including start and end dates, along with the progress of that Lesson; the 

Lesson progress represents the combined progress of all students assigned to a particular 

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Lesson. By selecting one or more Lessons via the checkbox to the left of a Lesson Name, an 

instructor may view the progress and available grading metrics for each student and each 

file contained with the selected Lesson(s). This data can be viewed in two ways: 

 View by Student (as seen in Figure 10) displays data files according to the 

individual student to which that file belongs. This view helps provide a snapshot of a 

student’s progress through a lesson along with any potential grading metrics that 

are available. 

Figure 10 - View by Student mode 

 View by Data File (as seen in Figure 11) displays the individual data files within a 

selected Lesson. Listed Lesson files can be expanded to display each student, 

including individual student progress and any available grading metrics for that 

particular file. This mode is especially useful for selecting multiple students’ markup 

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for the purposes of comparing and combining markup, and for facilitating 

discussions in a classroom setting. 

Figure 11 - View by Data File mode 

Regardless of how Lesson data is viewed in the Instructor dashboard, instructors have the 

ability to select multiple files for comparison using the overlay functionality in ULW Latent 

Editor. Similar to a student viewing their markup and the “ideal” markup as an overlay in 

Latent Editor, an instructor may view any number of markup files on a single image. 

Depending on the active view, instructors may select each student under a Lesson file in 

the View by Data File mode or the same files under each student in the View by Student 

mode. Figure 12 shows an example of multiple students selected using the View by Data 

File mode. 

Figure 12 - Multiple students selected in View by Data File mode 

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After making all of the desired selections, the Compare Files button located at the top of 

the Instructor dashboard can be used to open an instance of ULW Latent Editor that will 

display multiple sets of markup, one for each selection made in the Instructor dashboard, 

as an overlay on a single image, as shown in Figure 13. 

Figure 13 - Multiple student’s markup, including answer file, on a single image 

Viewing student markup in this way provides a basis for discussion within a classroom 

setting while maintaining anonymity among examiners and their peers. Instructors can 

toggle sets of markup on and off to compare and contrast the levels of detail marked by 

each examiner and to identify areas where improvement or additional focus may be 

required. If available, answer files will also be visible to compare groups of students against 

an “ideal” set of markup. Instructors also have the option of clustering student markup to 

display the consensus among examiners. Minutia clustering is described in detail in section 

2.3. 

2.1.2 Self-led training 

Examiners may also wish to test themselves through self-led training rather than 

participating in a classroom setting as previously described, or as a supplement to 

classroom training. Additional functionality has been built into ULW Latent Editor to 

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facilitate self-led training opportunities. The Latent Editor Training menu contains a new 

option that allows a user to load multiple files to compare against their current work. 

Figure 14 - Self-led training in Latent Editor

Loading multiple files in this way will open a second instance of the Latent Editor 

application in which the selected files and the examiner’s in progress work will be loaded 

as an overlay on a single image. This allows an examiner to compare themselves against 

available markup files without interfering with their work, giving them access to the same 

tools made available to instructor users. Individual users also have the option of creating 

their own data sets, lessons, and sessions, where they may act as the instructor and student 

user to facilitate self-led training from within Transaction Manager as described in Section 

2.1.1. Though this may not offer the same benefits as training led by an instructor, 

ACEware does not prohibit users from experimenting with their own personal data sets 

and lessons. 

2.2 Datasets 

The lack of sufficient research data is a major limitation to research, the development of new 

technology, and informed policy decisions in forensic science. In both forensic science and 

biometrics, datasets are necessary to conduct research and evaluations: to develop and test 

new technology, to evaluate human factors, and to provide a basis for data-driven policy 

decisions. Current research is impeded due to a limited quantity of appropriate research 

datasets. The Forensic Science and Standards Act of 2014 (Senate legislation S.2022, aka the 

“Rockefeller bill”, currently in committee), section 6, if passed will direct NIST to collect and 

provide such datasets. 

ACEware was developed to facilitate the development of standard datasets for training and 

evaluation. ACEware provides users with 

 A specification for friction ridge datasets that seeks to standardize the association of 

images and features for both latent and exemplar prints. The intent is to propose a 

standard specification for friction ridge datasets that can be used in the creation and 

dissemination of future datasets (see Appendix A: Data Set Directory Specification).

 Tools for creating datasets in accordance with the dataset specification. These tools can 

be used within agencies, for example to collect image pairs that resulted in errors or 

conflict among examiners for use in future training. 

 The NIST SD27/ULW Groundtruth/ELFT-EFS Public Challenge dataset, and Noblis MultiLatent Dataset formatted in accordance with the dataset specification. 

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The need for a specification to define the associations between files may not be initially 

obvious. For a dataset in which the only associations are between each latent and a single 

specified exemplar image, such a specification may be unnecessary. However, associations 

become more complex for datasets (such as the combined SD27/ULW Groundtruth/ELFTEFS Public Challenge dataset) with multiple latents per subject, multiple sets of exemplars 

per subject, and multiple markup files per latent. 

ACEware has added the ability to create and manage data sets from within ULW 

Transaction Manager. By creating data sets in Transaction Manager, users can import 

individual files, ranging from images to ANSI/NIST compliant file formats, into a selected 

data set row. Users may also choose to import an entire standard dataset formatted in 

accordance with the dataset specification. These import options are shown in Figure 15.

Figure 15 - Data set import options 

Data sets created or imported in ACEware will be displayed in the Data Sets panel, as seen 

in Figure 16, for management by one or more users. Data sets may also be shared among 

users via the Groups feature described in Section 2.1, and serve as the basis for Lesson 

content. 

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Figure 16 - Data browser containing multiple data sets 

File “relationships” can also be set within the user interface to create associations among 

various image and ANSI/NIST files. For example, associating a search to image file means 

that Search file X contains the markup for latent print Y. Relationships display as tooltips 

when hovering over relationship set icons as seen in Figure 17 and Figure 18. 

Figure 17 - Single data set relationship tooltip 

Figure 18 - Multiple data set relationships tooltip 

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2.3 Comparing and combining minutia markup 

Examiners often differ in the minutiae they mark.29 ACEware incorporates functionality to 

compare the markup on a single latent print by multiple examiners, or to create a single 

“voted” markup based on multiple examiners. This ability may be used for training, 

operational casework, or research: 

 For training, this allows students to be compared against an “ideal” markup, or against 

each other. 

 For training or research, this allows the development of “ideal” markup for datasets 

based on the consensus of multiple expert examiners. 

 For operational casework, this permits 

o Comparing an examiner’s Analysis markup and Comparison markup in order to 

assess if there are indications of excessive changes (extensive additions and deletions 

of features between Analysis and Comparison may be an indicator that the examiner 

was biased by the exemplar during Comparison30).

o Comparing the markup of an examiner and a verifier as part of conflict resolution 

(when the examiner and verifier reach different conclusions). 

o Creation of a consensus markup among multiple examiners for courtroom 

presentations for important cases. 

Initial feedback from ACEware testers is that several of these uses are expected to be 

valuable. 

ACEware implements a hardened version of an algorithm initially developed for use in 

research in the FBI Lab/Noblis Interexaminer variation of minutia markup on latent 

fingerprints study,31 which describes the algorithm in this way: 

Here, we use a commonly-used data clustering algorithm, Density-Based Spatial 

Clustering of Applications with Noise (DBSCAN), to classify minutiae marked by 

multiple examiners as representing the same minutia on the latent. The DBSCAN 

algorithm was parameterized with a reachability distance of 0.38mm (0.015 

inch)32; any marked minutiae within this distance of one another coalesce into a 

cluster (a cluster starts with an arbitrary marked minutia, grows to include any 

other marked minutiae (from all examiners) within that distance, and then 

iteratively grows to include any other marked minutiae within that distance of 

the cluster). As detailed in Appendix SI-4, small changes to the reachability 

parameter had a large effect on the total number of resulting clusters, especially 

with respect to singletons (clusters containing only one marked minutia). The 

distance of 0.38mm was selected after extensively reviewing the algorithm’s 

performance over a range of reachability settings. After performing this initial 

clustering, we then identified a relatively small number of clusters that had 

 

29 [Ulery16, Swofford13] 

30 [Ulery14b] 

31 [Ulery16] 

32 The distance between ridges varies within an impression and between subjects, but average peak-topeak distances are reported as varying between 0.43mm and 0.56mm [Hicklin13, Ashbaugh]. 

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grown excessively large: for example, a single minutia mark located between 

what would otherwise have been two distinct ridge event locations would cause 

those two clusters to coalesce. These “overgrown” clusters were split using 

agglomerative hierarchical clustering to produce the final set of clusters for 

analysis. 

For each fingerprint, the set of all minutiae x,y coordinates (as marked by the 

examiners) was preliminarily clustered using DBSCAN with a given radius r, and 

no lower limit to the cluster size. That is, singletons were treated as valid clusters, 

not labeled as “noise.”

The implementation of Density-based spatial clustering of applications with 

noise (DBSCAN) we used was written by Michal Daszkyowski of the University of 

Silesia in 2004. The DBSCAN radius was set to 0.015" (0.38mm) after extensively 

reviewing the algorithm’s performance over a range of radius settings. In our 

review, we considered several standard clustering performance measures and 

visually assessed the resulting clusters as plotted superimposed over the latent 

prints. Any choice of radius substantially biases the reproducibility distributions: 

increasing the radius increases the measured mean reproducibility, and 

decreases the measured number of clusters. We selected a slightly large radius 

in order to aggregate some of the less precisely focused clusters; we then split 

many of the oversized clusters in the second step. 

Oversized preliminary clusters were split using agglomerative hierarchical 

clustering, with ceiling (mean number of marks per examiner) as the cutoff 

point. Hierarchical clustering assembles a tree of cluster relationships; there is 

no assumption of a fixed radius. 

Oversized preliminary clusters were selected for subsequent splitting by 

agglomerative hierarchical clustering based on a criterion of (mean number of 

marked minutiae per examiner) > 1.5. This arbitrary threshold was selected 

because (1) automated splitting of clusters meeting this criterion was highly 

successful, and (2) for lower values (between 1 and 1.5), it was usually not 

apparent even to a human how to split correctly without careful interpretation 

of the fingerprint image. The oversized preliminary clusters often contained 

multiple, clearly distinct ridge events; most of the remainder were difficult to 

resolve by visual inspection. We used MATLAB’s implementation of 

agglomerative hierarchical clustering algorithm; Ward’s method was selected 

for computing the distance between clusters. Ward’s method helps overcome the 

main flaw of DBSCAN, which is that it tends to fail when faced with highly 

heteroskedastic data (data in which the variance differs among subsets). 

By loading multiple sets of markup on to a single latent image, we’re able to directly 

compare each set of markup against the other, toggling the visibility of the markup on 

and off to view markup on a one-to-one or one-to-many basis. Figure 19 shows five 

sets of markup selected for viewing, some of which may be difficult to see due to the 

close proximity of other markup sets. By selecting different rows, or toggling feature 

visibility on and off, more difficult to locate features can be seen with greater clarity 

as those features are brought to the front of the overlay. 

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Figure 19 - Five sets of markup viewed as an overlay on a single latent image 

The concept of comparing multiple sets of markup on a single latent image is one use-case 

of comparing and combining markup. As previously noted, this functionality may also be 

used to create “ideal” markup based on multiple expert examiners. Continuing with the 

example markup in Figure 19, Figure 20 shows a menu containing various combinations of 

clusters for generating and displaying “ideal” markup.

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Figure 20 - Clustering options 

Clusters are split into four categories: 

 Super majority clusters are composed of features in which consensus was found 

among 75% or more of the analyzed examiners 

 Majority clusters are composed of features in which consensus was found among 

less than 75% but more than 50% of the analyzed examiners 

 Minority clusters are composed of features in which consensus was found among 

less than 50% but more than 1 of the analyzed examiners 

 Singleton clusters are composed of features in which only 1 examiner marked that 

feature 

As an example, Table 1 shows a possible distribution of clusters based on a number of 

analyzed examiners and the consensus among those examiners. When clustering markup 

from five examiners, it would be expected that consensus among four or more examiners 

would comprise super majority clusters, consensus among three examiners would 

comprise majority clusters, consensus among two examiners would comprise minority 

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clusters, and markup with no consensus, meaning only one examiner marked a particular 

feature, would comprise singleton clusters. 

Super Majority Majority Minority Singleton

# of Examiners

3 3 2 - 1

4 4 3 2 1

5 4+ 3 2 1

Table 1 - Sample cluster distribution 

Using ACEware’s minutia clustering algorithm, we’re able to analyze the selected 

examiners’ markup and display feature clusters according to the option selected in the 

Perform Clustering menu shown in Figure 20. Users are able to select different 

combinations of markup and view different combinations of clusters until the desired 

outcome is achieved. Figure 21 shows the results of displaying super majority clusters 

based on the previously selected five sets of markup. 

Figure 21 - Voted markup results only 

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The results of markup clustering can be saved to an LFFS file for future use in training 

or research. Also available is the option to save the clustering results, as well as the 

selected sets of markup, to the EFS defined ASYS file for future analysis and exchange 

between organizations. 

For comparison, Figure 22 shows the voted markup results in addition to all of the markup 

that went into creating those results. Due to the close proximity of the visible markup, 

some features are hidden or otherwise difficult to see. 

Figure 22 - Voted markup results with additional markup 

From the examples in Figure 19, Figure 21, and Figure 22, we can start to see how different 

combinations of markup, and different levels of clustering, can help create a combined set 

of potential “ideal” markup. To further understand how individual features are factored 

into the results of minutia clustering, we can switch our view from coloring features based 

on individual files to coloring features based on consensus levels by selecting the Color by 

Consensus option. Figure 23 shows the same markup displayed in Figure 19, colored by 

consensus rather than by file. In this view, we can now see the individual features that were 

factored into producing the results of the super majority cluster results seen in Figure 21, 

specifically. Had a clustering option other than super majority been selected in the 

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examples above, we could expect to see clustering results based on features displayed in 

yellow (majority clusters), orange (minority clusters), and even red (singleton clusters). 

Figure 23 - Markup colored by consensus 

Feature comparison and combination through clustering is available through 

ACEware’s data set based Training functionality and through Latent Editor’s Training 

menu, as detailed in section 2.1. 

2.4 Enhancements to enable non-AFIS casework 

ULW already has extensive AFIS-specific functionality, much of which is appropriate for nonAFIS casework. A small number of relatively minor enhancements to ULW have made ULW 

more useful for non-AFIS casework. 

2.4.1 Verifications 

As part of ACEware’s goal to support the ACE-V process, the ability to perform verifications 

from within the ULW software has been introduced. 

As seen in Figure 24, administrative users are presented with the option of selecting a 

combination of blind or non-blind verification methods, with neither or both options as 

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Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

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25

viable selections. Each verification method is selected for each possible determination, or 

comparison conclusion, that can be set within the ULW Comparison Tool. In a blind 

verification, the original examiner’s markup and conclusion are removed and the verifying 

examiner is given only the latent print and a single candidate exemplar; in a non-blind 

verification, the verifying examiner is given a copy of the original examiners’ work, 

including all marked features and conclusion, in order to review the work performed. 

Figure 24 - Verification configuration 

For determinations with no option selected, no verification will be created as a result of 

selecting that determination in Comparison Tool. Determinations with a single option 

selected will automatically generate a verification of that type while determinations with 

both options selected will prompt the user to select a verification method upon verification 

assignment. 

When an examiner compares a latent print search against potential exemplar candidate 

matches within the Comparison Tool, each determination requiring verification made by 

the examiner will cause the Verifications count in the Transaction Manager User Data Grid 

to increase. Figure 25 shows an example in which three verifications have been created for 

case B_118_0_1, noted in the Verifications column. This column is only seen by 

administrative users and serves as a flag indicating a need for verifications to be assigned 

and monitored. 

Figure 25 - Verifications count 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
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26

Verifications can be assigned to another examiner from within the Verification preview 

panel, accessed by clicking on the Verification count for a particular case. When assigning 

verifications, administrative users are presented with a list of possible assignees based on 

the user accounts created in Transaction Manager. In the event both blind and non-blind 

verification methods were selected for the determination in question, users will be 

required to select both an assignee and a verification type to complete the assignment 

process, as seen in Figure 26. 

Figure 26 - Assigning verifications 

When a verification is successfully assigned, the assignee receives a new task in his or her 

Task List as shown in Figure 27; these tasks are accessible from ULW Transaction Manager, 

Latent Editor, and Comparison Tool. Verification assignments are kept separate from the 

verifying examiners casework and act as single events in which examiner action is needed. 

Figure 27 - Verification task 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

27

Verifying examiners can open an assigned verification by double-clicking on the task to 

open the appropriate file and perform the necessary level of verification. Upon completion, 

verifying examiners may save their work and the verification is complete. Both the original 

examiners work and the verifying examiners work are stored as separate COMP files within 

Transaction Managers Transaction folder to maintain the work performed by each 

examiner. After a verifying examiner completes the verification process, the original 

examiner and the verifying examiner’s determination will be compared and the status of 

the verification will change to one of three potential options: 

 Verified is a result of the original examiner and verifying examiner marking the 

same determination 

 Conflict is a result of the original examiner and verifying examiner reaching 

dissimilar conclusions, for example Exclusion vs. Inconclusive 

 Critical Conflict is a result of the original examiner and verifying examiner reaching 

dissimilar conclusions, for example Exclusion vs ID/Preliminary ID (and vice versa); 

this level of conflict may pose significant risk if not evaluated and is therefore 

flagged as critical. 

Figure 28 - Completed verification statuses 

In the event any conflict status is generated, administrative users may begin a conflict 

resolution process. 

2.4.2 Individual feature annotations 

Understanding an examiner’s thought process or rationale for marking particular features 

is a useful tool in determining whether sufficient detail has been provided in the latent 

print examination process. ACEware introduces a new Enter Comment option when 

examiners right-click on points marked in Comparison Tool, shown in Figure 29. 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
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28

Figure 29 - Enter comment option in Comparison Tool 

By selecting the Enter Comment option, examiners are presented with a blank, free text 

form in which comments about that particular feature can be entered. 

Figure 30 - Commenting on individual features 

Entered comments will then display when examiners hover over a point for which a 

comment has been made. 

Figure 31 - Viewing a feature-level comment 

Individual comments are viewable by verifiers in non-blind verifications and may be used 

to better understand an examiner’s rationale for individual feature markings.

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Department of Justice. 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 
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Final Report 

29

2.4.3 Quality assurance reports 

One of the key implications of standardized feature markup is that it would enable 

automated quality assurance (QA). As part of ACEware we implemented a limited amount of 

QA reporting — both as useful tools, and as examples of what automated QA could be 

possible. 

Figure 32 - Reports tab in Transaction Manager 

Under ACEware, ULW Transaction Manager now has Quality Assurance Reports, which list 

those cases that require “review” or “rigorous review” based on marked minutiae and 

detected changes between Analysis and Comparison markup:33

 Rigorous Review: Identifications where fewer than 7 minutiae were marked, or where 

fewer than 7 minutiae were retained between Analysis and Comparison 

 Review: Identifications where fewer than 12 minutiae were marked, or where fewer than 

12 minutiae were retained between Analysis and Comparison 

 

33 The 7- and 12-minutia thresholds are based on the results of the White Box study [Ulery14a]. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
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30

Figure 33 - Quality Assurance Report example 

Markup Summary Reports display the distribution of features with respect to the examiners’ 

assessments of value and comparison conclusions. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

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31

Figure 34 - Markup Summary Report example 

2.4.4 Expansion of EFS fields 

Expanding ULW’s support of the extended feature set, Comparison Tool now has access to 

several EFS fields such as Latent Substrate, Latent Matrix, Latent Processing Method, 

Method of Feature Detection, Evidence of Fraud, etc., that were previously only available in 

Latent Editor. Adding support for these fields in Comparison Tool gives examiners an 

opportunity to refer to EFS data during comparison, or to enter EFS data if it does not 

already exist, thereby expanding the boundaries of detailed documentation. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

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32

Figure 35 - Blank EFS Fields in Comparison Tool 

Any data entered during comparison is saved to COMP files for record keeping and data 

exchange between organizations. 

3 Dissemination 

The purpose of this project is to deliver software and work with agencies to ensure its 

adoption. The project involved collaboration with the law enforcement agencies that were 

our official partners (Fairfax County Police Department, New York State Division of Criminal 

Justice Services, and Arizona Department of Public Safety Crime Laboratory), as well as 

additional testers (FBI Laboratory, Royal Canadian Mounted Police, and Cuyahoga County 

Regional Forensic Science Laboratory). The partners and testers provided input on initial 

requirements as well as assisted with the refinement of requirements. 

An Alpha test version was released for feedback in January 2015. Beta test versions were 

released in March 2015, late November 2016, and early December 2016. 

In depth demonstrations of functionality provided to multiple organizations (including FBI 

Lab, Cuyahoga County Regional Forensic Science Laboratory, Fairfax County Police 

Department, New York State Division of Criminal Justice Services, Arizona Department of 

Public Safety Crime Laboratory) provided a means for obtaining immediate feedback for new 

requirements or improvements/modifications to existing requirements based on 

organizational needs. These demonstrations also provided beta testers with an opportunity 

to attain basic familiarity with ACEware features to help ease the transition into testing. 

The November/December 2016 Beta test version of ACEware, named ULW with ACE-V 

Enhancements, was provided after functional demonstrations to give testers an opportunity 

to get hands on experience with a version of the software that was considered feature 

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Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

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33

complete. Feedback from demonstrations and beta testing directly impacts the need for the 

final release, prior to the conclusion of the project as well as the content of those releases. 

Other ACEware presentations have been given in the form of conference briefings, including: 

 American Academy of Forensic Sciences 68th Annual Scientific Meeting in Las Vegas, NV 

- February 2016 

 International Association for Identification 101st International Forensic Educational 

Conference in Cincinnati, OH - August 2016 

4 Conclusions 

4.1 User feedback 

The reactions to the ACEware functionality have been positive with users noting multiple 

use cases where ACEware would either improve current processes or introduce new 

processes that would be beneficial for past, current, and future work. 

Many testers believe data set development and management tools introduced by ACEware 

will provide a platform for compiling and storing casework in a standard format and will act 

as a valuable tool for retrieving such data for later use. Testers also noted the potential for 

data set file relationships to serve as a valuable tool in associating data within large cases 

and may even provide useful tools for assisting in conflict resolution by associating original 

and verifying examiner data to be tracked both within and outside of the software 

application. Interest was shown in enhancing data set development tools to support custom 

relationship options (e.g., same latent image resulting from different lift methods) as well as 

batch relationship assignment for faster data set file association.

The training functionality introduced by the ACEware project was also very well received 

with a number of testers seeing great value in the overlay functionality. In one example, 

testers noted overlays are currently used to facilitate conversations with trainee and active 

case working examiners but in the absence of latent print specific tools, third-party 

programs such as Photoshop are necessary to produce useful results, a process which can 

take hours depending on the number of examiners involved. With ACEware, overlays can be 

created in a fraction of the time. Testers were also interested in the possibilities afforded by 

data set driven training with the ability to create reusable lesson plans for training examiners 

on special cases (e.g., distortion, scars, or digital artifacts) and image processing techniques 

(e.g., tonal reversal). 

Quality assurance reporting features were generally well received though many testers 

showed an interest in more generalized reporting capabilities such as the ability to select 

from a list of available data points to generate a simple output of all data related to cases 

within the Transaction Manager tool. However, other testers noted the potential for quality 

assurance reports to provide a basis for returning to files previously submitted to the 

Unsolved Latent File (ULF) for further analysis and potential re-submission in an effort to 

lower the number of searches submitted to and retained in the ULF. Testers also saw value 

in using the quality assurance reports to gauge the effectiveness of individual examiners and 

identifying areas where additional training may be necessary or valuable. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

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34

ACEware’s verification features received favorable reviews overall though feedback to date 

suggests additional work is necessary to offer a more robust solution that fits the various 

needs of multiple organizations. While the ACEware verification functionality has room to 

grow, feedback also indicates ACEware introduces basic verification procedures that can be 

adopted by organizations that do not yet have a standard verification process in place, 

increasing the use of standard ACE-V processes. 

For specific comments and enhancement suggestion, see Appendix B: Compliments and 

Enhancement Suggestions. 

4.2 Impact 

4.2.1 What is the impact on the criminal justice system? 

Whether, or how, latent print examinations are documented in detail greatly varies among 

agencies in the criminal justice system. ACEware seeks to provide improved documentation 

tools; training on standardized, detailed documentation practices; and enhanced latent print 

examination tools that support conflict resolution, quality assurance, and verification of 

latent print examiner work. By making these tools readily available to state, local, and federal 

agencies, ACEware will help increase the rigor applied to the examination process in an effort 

to decrease the risk of false or missed identifications. 

4.2.2 How has it contributed to crime laboratories? 

ACEware will contribute to the improvement of detailed latent print examination 

documentation through the training of standard latent print markup and documentation 

practices; improved latent print examination tools; and adherence to pre-existing standards 

that support rigorous documentation, data exchange, and long-term archiving. 

4.2.3 What is the impact on technology transfer? 

ACEware expands on the Universal Latent Workstation software. Enhancing and adding to 

the capabilities of pre-existing, widely used software, will have an immediate impact on 

users by providing new tools in familiar applications. While adoption rates may vary among 

agencies, those that do incorporate ACEware into daily operations will help build a baseline 

for detailed documentation practices that will be consistent across participating agencies. 

4.3 Implications 

Beyond the specific functionality implemented in ACEware, adoption of ACEware is 

intended to improve the rigor, transparency, standardization, and quantifiability of the 

latent print examination process: 

 Provide a standardized approach for applying the ACE-V method, building upon 

SWGFAST standards and EFS 

 Facilitate increased consistency and proficiency in feature selection and detailed 

documentation by latent print examiners 

 Improved resolution of disagreements between examiners and verifiers (conflict 

resolution) 

 Standardize formats for data exchange, long-term data archiving, and evidentiary 

presentations 

 Provide a standard platform for collection of data and performance metrics 

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necessarily reflect the official position or policies of the U.S. Department of Justice 
ACEware Latent Fingerprint Identification Research and Software Development 

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35

 Provide a basis for quantitative analyses of latent print examination, including automated 

metric-based quality assurance, and quantitative examiner performance evaluations 

After the release of ACEware and completion of the development phase, future research 

can evaluate its effectiveness, and develop additional quantitative analyses of latent print 

examination. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

36

5 References 

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Advances in Fingerprint Technology, 2nd Ed. Boca Raton: CRC Press, pp 327-387.

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by Latent Fingerprint Examiners. PLoS ONE 7:3. 

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Department of Justice. 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 
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6 Appendix 

6.1 Appendix A: Data Set Directory Specification 

Purpose

The purpose of the Data Set Directory Specification is to define a file format and structure 

for the development of standardized friction ridge data sets that enables data formatting to 

be recognized across multiple jurisdictions to increase the potential for data exchange in 

support of training or research. 

Data Set Directory Structure

The DataSet directory must conform to the hierarchy as shown below. If there are no 

relevant files for a particular directory or subdirectory (e.g., no files to place in the ANFiles 

folder) the directory or sub-directory may be omitted. 

 Datasets 

o Dataset Name (e.g. SD27) 

 ANFiles 

 CandLists 

 Comps 

 LatentANs 

o LatentAN-Image 

o LatentAN-ImageMarkup 

o LatentAN-Markup 

 SubjectANs 

o SubjectAN-Set 

o ExemplarAN-Image 

o ExemplarAN-ImageMarkup 

o ExemplarAN-Markup 

 Images 

 ExemplarImages 

 LatentImages 

 OriginalImages 

 Documentation 

 References.txt 

 InheritedData.txt 

 Subjects.txt 

 InheritedSubjects.txt 

 Metadata.txt 

 Xref.txt 

 Ver_NAME__DATEorVNum.txt 

As an example, the following image shows a folder structure based on the structure 

outlines above. The left side of the image displays the hierarchy of folders while the right 

side of the image displays the contents of the root SampleDataset folder. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

40

Figure 36 - Graphical View of Data Set Directory Hierarchy 

Data Set Directory Descriptions

Datasets

Root of directory tree, must always be present. 

Dataset Name (e.g. SD27) 

This is one data set's directory. Files that are inherited from other data sets are listed in 

InheritedData.txt, while files that are new or modified from their sources are in Images if 

pure image files and ANFiles if transaction information is attached. Different subjects with 

images or AN files in the directory are given unique identifiers and listed in Subjects.txt. 

Relationships between images, AN files and subjects in the data set are delineated in 

Xref.txt using the list of possible relationships defined in the comments there. Names must 

contain no spaces. Only permissible symbols are - and _ (for multiplatform use). Double 

underscores (__) and double dashes (--) are not permitted. Any files not of the specified 

types must go in Documentation. 

ANFiles 

Subdirectories under this directory contain ANSI/NIST files, which include images and/or 

markup, and possibly other information (e.g. demographics). All files must be valid 

ANSI/NIST files. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

41

CandLists 

Contains AN files with one or more candidates contained in a candidate list, including 

match scores and other relevant transaction information. SRL files go here. Files always 

contain images (generally exemplars but sometimes latents); file may contain markup in 

type-9 record. Files may be contained in dataset-specific subdirectories starting with "_" 

(no spaces, no symbols other than - and _); no further subdirectories are permitted. 

Comps 

Contains files with one probe image (sometimes referred to as "the latent," although it may 

not be the only latent) and one or more candidate images (sometimes referred to as 

"exemplars," although they may not always be). May contain feature markup, including 

corresponding points between pairs of prints. COMP format is defined in LITS. 

LatentANs 

Subdirectories contain AN files with latent images and/or markup. 1 image per file. 

LatentAN-Image 

Latent AN files that only have images (no markup), i.e. LFIS. 1 image per file. Files may be 

contained in dataset-specific subdirectories starting with "_" (no spaces, no symbols other 

than - and _); no further subdirectories are permitted. 

LatentAN-ImageMarkup 

Latent AN files that have markup AND images. 1 image per file. Files may be contained in 

dataset-specific subdirectories starting with "_" (no spaces, no symbols other than - and _); 

no further subdirectories are permitted. 

LatentAN-Markup 

Latent AN files that only have markup (no images). 1 image per file. Files may be contained 

in dataset-specific subdirectories starting with "_" (no spaces, no symbols other than - and 

_); no further subdirectories are permitted. 

SubjectANs 

Subdirectories contain AN files with Exemplar images, sometimes markup, for ONE person 

per file (see "Candlists" for multiple people per file). Multiple things related to a single 

person (i.e. results of an IRR linked to original image to a candidate list). 

SubjectAN-Set 

Exemplar AN files with images for multiple finger/palm positions, not just the specific 

position corresponding to the probe latent. Generally contains 14 images (10 rolled + 4 

plain). May contain markup (in type-9 records). One subject, multiple finger/palm 

positions. Files may be contained in dataset-specific subdirectories starting with "_" (no 

spaces, no symbols other than - and _); no further subdirectories are permitted. 

ExemplarAN-Image 

Exemplar AN files that have images (no markup). One subject, one image per file. 

Finger/palm position corresponding to the probe latent. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

42

ExemplarAN-ImageMarkup 

Exemplar AN files that have images AND markup. One subject, one image per file. 

Finger/palm position corresponding to the probe latent. Files may be contained in datasetspecific subdirectories starting with "_" (no spaces, no symbols other than - and _); no 

further subdirectories are permitted. 

ExemplarAN-Markup 

Exemplar AN files that have markup (no images). One subject, one image per file. 

Finger/palm position corresponding to the probe latent. Files may be contained in datasetspecific subdirectories starting with "_" (no spaces, no symbols other than - and _); no 

further subdirectories are permitted. 

Images 

Directory containing subdirectories of images within a dataset. 

ExemplarImages 

Exemplar image files that are 500 or 1000ppi and grayscale. Files may be contained in 

dataset-specific subdirectories starting with "_" (no spaces, no symbols other than - and _); 

no further subdirectories are permitted. 

LatentImages 

Latent image files that are 500 or 1000ppi and grayscale. Files may be contained in 

dataset-specific subdirectories starting with "_" (no spaces, no symbols other than - and _); 

no further subdirectories are permitted. 

OriginalImages 

This folder contains images that may be color, various resolutions, may contain background 

or multiple impressions. (e.g., an image from a crime scene containing several latent 

prints). When OriginalImages are present, Images in the other directories will reference 

these as their source. Files may be contained in dataset-specific subdirectories starting 

with "_" (no spaces, no symbols other than - and _); no further subdirectories are permitted. 

Documentation 

Any files that are not otherwise specified must go in the Documentation directory. May 

contain subdirectories or any type of files. A file named README (.txt, .doc(x), .pdf all 

acceptable) is mandatory. 

References.txt 

Lists other datasets used, including version. This file is required if InheritedData.txt OR 

InheritedSubjects.txt is present, and otherwise is not included. No path information is 

included. Lines starting with # are comments and can be ignored. 

InheritedData.txt 

Contains path information for data inherited from other data sets. Files included by 

reference here are treated as if they are fully part of this dataset, and can be included in the 

Subjects and Xref files. Paths are relative to this directory and always start "../" (e.g. 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

43

"../SD27/ANFiles/Comps/L001g.comp"). Paths include forward slashes for platform 

independence. Lines starting with # are comments and can be ignored. 

Subjects.txt 

Text file containing the unique subject identifiers for all subjects contained in this dataset. 

Subject IDs may not contain spaces, cannot be case sensitive (capitalization is permissible 

but only for clarity); the only permissible symbols are - and _; double underscores (__) and 

double dashes (--) are not permitted. Each line contains one subject ID and nothing else. 

Other datasets may refer to these subjects by reference by using the dataset name followed 

by a double dash (--) followed by the subject ID. This only includes subjects that are not 

inherited from other datasets: see also InheritedSubjects.txt. {Leading zeroes must be 

preserved; otherwise sorting and uniqueness when used as a reference across datasets 

would become broken (e.g. "BDM--001" should not be reduced BDM--1")}. It is 

recommended that all subject IDs for a dataset be the same length. # may be used on any 

line to create a comment. 

InheritedSubjects.txt 

This contains subjects inherited from other datasets. The use is identical to Subjects.txt, but 

each line starts with a dataset name (that cannot be this dataset), followed by "--" and the 

subject ID as used in that dataset. The References.txt file must include the name and 

version of each dataset. (Special case: if this dataset refers to dataset A, and dataset A 

inherits subjects from dataset B, then this dataset must include a reference to dataset B, 

and the subject IDs would all be prefixed "B--"). Note that Subjects can be inherited even if 

the referenced dataset is not present. # may be used on any line to create a comment. 

Metadata.txt 

Tab-delimited file containing filename, subjectID, and other metadata. Generally all files 

that contain a single subject will indicate subject (if known). Other columns are optional 

(they are there to provide a standard means of indicating metadata for image files). Lines 

starting with # are comments and can be ignored. (See "Metadata descriptions") 

Xref.txt 

Tab-delimited text containing all known relationships between items in the data set 

(including items included by reference), using a tab-delineated row of ChildFile, ParentFile, 

Relationship and (optional) RelationshipDetail. See "Xref descriptions" for detail. # may be 

used on any line to create a comment. 

Ver_NAME__DATEorVNum.txt 

Mandatory. The name of this file indicates the version of the dataset. The dataset name 

(identical to the containing directory name) is followed by a double underscore (__) and a 

date or number indicating the specific version. Whenever the contents of a dataset are 

changed, the name of this file must be changed. The contents of this file are irrelevant 

though it is suggested "Dataset configuration file: do not delete" be included so the file does 

not appear as empty.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

44

Xref descriptions 

The image below details the relationships that may result from files contained within the data set directory. As an example, 

when viewing the MarkupToPrint column, files contained within LatentAN-Markup or ExemplarAN-Markup will act as the 

“from” file while files contained in ExemplarImages or LatentImages will act as the “to” file. Thus, files from LatentANMarkup may have a MarkupToPrint relationship to files contained within LatentImages. 

MarkupToPrint LatentToCandidateList LatentToCandidate ImageToImageSet ImageIdentical MarkupIdentical SourceToDerivedImage SameSourceImage SourceToDerivedMarkup

from: markup; 

to: print

from: latent; to: list of 

candidates (potential 

matches)

from: latent; to: 

single candidate 

(potential match; 

used to indicate 

things like close 

nonmatches)

from: single 

image; to: AN file 

containing that 

exact image 

(among other 

images)

from/to: images 

are identical in 

every way (each 

file contains 1 

image)

from/to: markup 

is identical in 

every way (e.g. 

special case of 

associating an 

LFFS with image 

to the same LFFS 

without image)

from: source image; to: 

derived image (e.g. 

cropped, rotated, 

compressed)

from/to: images 

came from the same 

source, e.g. both 

scans of the same 

paper photo (each 

file contains 1 

image)

from: source markup; to: 

modified markup (e.g. 

2nd examiner modified)

CandLists to to to

Comps to to

LatentAN-Image from from from to;from to;from to;from

LatentAN-ImageMarkup from from from to;from to;from to;from to;from to;from

LatentAN-Markup from from from to;from to;from

SubjectAN-Set to

ExemplarAN-Image from to;from to;from to;from

ExemplarAN-ImageMarkup from to;from to;from to;from to;from to;from

ExemplarAN-Markup from to;from to;from

ExemplarImages to from to;from to;from to;from

LatentImages to from from from to;from to;from to;from

OriginalImages from to;from

Examples LFFS to image LFFS to SRL

latent to exemplar 

that was false ID by 

an examiner 

LFFS to IRR LFIS to image

LFFS with image to 

the same LFFS 

without image

cropped image to 

uncropped image image to image

Permissible relationships in the Xref file

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

45

Metadata Descriptions 

The table below outlines the possible categories that may be used when creating the 

Metadata.txt file. 

Filename Always starts with "ANFiles/", "Images/", or "../"

SubjectID Entry from Subjects.txt or InheritedSubjects.txt

Position Finger/palm/plantar numeric code from AN2011/2013 Table 8. 

Fingerprints are generally 1-10 if known, 0 if unknown.

Segment Only used if Position = [0..10]. Alphanumeric code from 

AN2011/2013 Table 9. Finger segment is only indicated if the 

position is a finger code (0-10), and the finger segment is other than 

DST (fingerprints are assumed to be distal segment). 

RolledPlain Only used for exemplars. Values: ["Rolled" "Plain"]

InkLivescan Only used for exemplars. Values: ["Ink" "Live"]

LatentProcessing Only used for latents. Alphanumeric code from AN2011 Table 48 (for 

field 9.354 EFS latent processing method)

Substrate Only used for latents. Alphanumeric code from AN2011 Table 51 (for 

field 9.355 EFS latent substrate)

Matrix Only used for latents. Alphanumeric code from AN2011 Table 52 (for 

field 9.356 EFS latent matrix)

Comment Free text for other descriptors of the file such as collection particulars 

or assessment of difficulty. Limited to 7-bit ASCII text. Tabs and 

carriage returns are not permitted. 99 character max.

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

46

6.2 Appendix B: Compliments and Enhancement Suggestions 

This section is intended to provide both compliments received as well as suggestions for 

future enhancements based on tester feedback. Listed enhancements were found 

impractical to complete within the original period of performance or were out of scope. If 

further development is performed in the future, enhancement suggestions may be 

considered as areas to explore. 

6.2.1 Compliments Received 

 Cuyahoga County Regional Forensic Science Laboratory 

o I love the training aspect. I usually use Photoshop to create an overlay of 

feature markup which can take an hour or more for eight examiners. ACEware 

would make that process so much easier. 

o Even if we didn't use it for casework, we could see ACEware being useful for 

storing case data for future review. 

o Marking minutiae is intuitive and easy. 

 Arizona Department of Public Safety Crime Laboratory 

o If ACEware can serve as a central hub between multiple systems, it would solve 

a lot of problems with keeping data organized. 

 New York State Division of Criminal Justice Services 

o Very impressive - incredible tool. 

o Having meetings with examiners to discuss difficult prints would be much 

easier with the overlay functionality. 

o Absolutely see value in ACEware, especially with the training functionality. 

o The reports functionality would be really useful in determining if searches 

we’ve submitted to the ULF contain sufficient markup. 

 Fairfax County Police Department 

o Really like the concept of using datasets to create standard, reusable lesson 

plans for users (e.g. lesson on tonal reversal). 

o The information provided in the quality assurance reports would help identify 

examiners who may need more training or help us understand who isn’t 

working as efficiently as they could. 

 FBI Laboratory

o The dataset functionality would be a great tool for compiling casework– from 

start to finish - especially for large cases. 

6.2.2 Enhancement Suggestions 

 Allow individual examiners to assign verifications to other users. 

 Allow verifications to be assigned in batches to better support large cases. 

 Track multiple verifications made by different examiners to support double 

verifications (e.g., file is assigned to a second examiner after the first verifier has 

completed work). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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 
ACEware Latent Fingerprint Identification Research and Software Development 

Final Report 

47

 Allow verification results to be configured so individual organizations can define 

what results in a Critical Conflict/Conflict/Verified status. 

 Maintain snapshots of quality markup over time to see progression of quality 

markup throughout the markup process. 

 Allow reports to be generated based on selection of available data sources (e.g., Case 

ID, Case Prefix, Search result count, etc.) and output to a simple Excel file for record 

keeping. 

 Display individual feature annotations made within Comparison Tool on printable 

reports 

 Allow auto-minutiae functionality to be turned off for training purposes. 

 Support custom relationship types for data set file association (e.g., same print lifted 

different ways and photographed from different angles) 

 Display image quality map for individual users when comparing and combining 

markup. 

 Allow users to be added through Windows Active Directory services. 

 Enable case data to be pulled from other sources (LIMS). 

This resource was prepared by the author(s) using Federal funds provided by the U.S. 

Department of Justice. 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