NIST Special Database 32

Multiple Encounter Dataset II (MEDS-II) 

Data Description Document

Andrew P. Founds

Nick Orlans

Genevieve Whiddon

MITRE Corporation 

Craig Watson

Information Technology Laboratory 

NISTIR 7807
NISTIR 7807

NIST Special Database 32

Multiple Encounter Dataset II (MEDS-II) 

Data Description Document

Andrew P. Founds

Nick Orlans

Genevieve Whiddon

MITRE Corporation 

Craig Watson

Information Technology Laboratory 

Information Access Division

February 2011

U.S. Department of Commerce

National Institute of Standards and Technology

Patrick D. Gallagher, Deputy Director
Acknowledgement

This dataset is being released (as prepared by MITRE Corporation) to support the NIST MultipleBiometric Evaluation 2010 (MBE). In addition, this dataset is available to any user interested in 

biometric research. The sponsor of this joint effort and provider of the data is the Federal Bureau of 

Investigation (FBI).

Disclaimer

Specific hardware and software products identified in this report were used in order to process and 

analyze the dataset described in this document. In no case does identification of any commercial product, 

trade name, or vendor, imply recommendation or endorsement by the National Institute of Standards and 

Technology, nor does it imply that the products and equipment identified are necessarily the best 

available for the purpose.
M TR 1 0 0 4 3 9

MI TR E T E CH NI CA L R EP OR T

Multiple Encounter Dataset 

(Deceased Persons) 

MEDS-II 

Data Description Document

Andrew P. Founds

Nick Orlans

Genevieve Whiddon

Version 4.0

December 22, 2010

M TR 1 0 0 4 3 9

MI TR E T E CH NI CA L R EP OR T

Multiple Encounter Dataset 

(Deceased Persons)

MEDS-II

Sponsor: FBI CJIS

Dept. No.: G123

Contract No.: J-FBI-07-164

Project No.: 1410FC09

Downgrade UNCLASSIFIED

Derived By: 

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The views, opinions and/or

findings contained in this report

are those of The MITRE Corporation 

and should not be construed as an 

official government position, policy, or 

decision, unless designated by other 

documentation.

This technical data was produced for 

the U. S. Government under contract JFBI-07-164 and is subject to the Rights 

in Data-General Clause 52.227-14, Alt. 

IV (JUNE 1987).

This document was prepared for 

authorized distribution only. It has 

not been approved for public release.

©2010 The MITRE Corporation. 

All Rights Reserved.

Data Description Document

Andrew P. Founds

Nick Orlans

Genevieve Whiddon

Version 4.0

December 22, 2010

Table of Contents

1 Overview...............................................................................................................................................................4

2 Data Preparation Methodology...................................................................................................................5

2.1 EBTS Data Decomposition..................................................................................................................5

2.2 Data Normalization and Correction................................................................................................6

2.3 Face Detection and Pose Labeling ...................................................................................................6

2.4 Recidivists and Match Pairs................................................................................................................7

3 Description of Corpus......................................................................................................................................8

3.1 Race and Gender......................................................................................................................................8

3.2 Age Summary and Time between Encounters ...........................................................................8

3.3 Image Dimensions ...............................................................................................................................10

3.4 Face Resolution and Subject Pose.................................................................................................11

4 Face Landmarking .........................................................................................................................................12

4.1 Landmarking Process.........................................................................................................................12

4.2 Landmarking Results..........................................................................................................................14

4.3 Next Steps for Landmarking and the MEDS-II Corpus.........................................................16

5 Face Recognition and Imposters .............................................................................................................16

Appendix A - List of Acronyms...........................................................................................................................19
List of Figures

Figure 1 – Relationship between Subjects, Submissions and Biometric Samples .........................6

Figure 2 – Distribution of Gender by Race.......................................................................................................8

Figure 3 – Histogram of Subject Ages at Photo Date...................................................................................9

Figure 4 – Times between Encounters (e.g., first and last)......................................................................9

Figure 5 – Image Dimensions .............................................................................................................................10

Figure 6 – Distribution of Estimated Interocular Distances (in pixels)...........................................11

Figure 7 – Face Roll Angle (degrees from horizontal).............................................................................11

Figure 8 – Example Stasm Outputs ..................................................................................................................12

Figure 9 - Depiction of 68 Stasm Points.........................................................................................................13

Figure 10 - Proportion of Automated vs. Manual Landmarking .........................................................14

Figure 11 – Ability to Automatically Landmark Based on Race..........................................................14

Figure 12 - Ability to Automatically Landmark by Gender ...................................................................15

Figure 13 – Correlation of PittPatt Confidence Scores with Automated Landmarking ............15

Figure 14 – Correlation of PittPatt Yaw Estimate with Automated Landmarking......................15

List of Tables

Table 1 – MEDS-II Dataset Overview.................................................................................................................4

Table 2 – Tools used to parse and examine submission files..................................................................5

Table 3 – Number of Type-10 Images and Submissions ...........................................................................6

Table 4 – Image Types ..............................................................................................................................................7

Table 5 – Enumeration of Match Pairs ..............................................................................................................7

Table 6 - Listing of 68 Stasm Points.................................................................................................................13

Table 7 – False Hits .................................................................................................................................................16

Table 8 – Results of Queried Images ...............................................................................................................17
Multiple Encounter Dataset (MEDS-II)

4

1 Overview

This document and associated dataset is an update to the Multiple Encounter Dataset I 

(MEDS-I), originally published by the National Institute of Standards and Technology (NIST)

in May 20101. The MEDS is a test corpus organized from an extract of submission files of 

deceased persons with prior multiple encounters. A submission file is an electronic file 

containing biographic and biometric data recorded during an encounter of an individual. 

The submission files conform to the specifications defined by the Electronic Biometric 

Transmission Specification (EBTS) extension to the American National Standards Institute

(ANSI)/NIST Information Technology Laboratory (ITL)-1-2007 standard2.

MEDS-I and MEDS-II are intended to stimulate research and to assist with the NIST Multiple 

Biometric Evaluation. The MEDS-II update approximately doubles the number of images, 

and extends the metadata to better support research and evaluation on pose conformance 

and local face features. These data are provided to assist the FBI and partner organizations 

refine tools, techniques, and procedures for face recognition as it supportsNext Generation 

Identification (NGI), forensic comparison, training, analysis, and face image conformance 

and inter-agency exchange standards. The MITRE Corporation (MITRE) prepared MEDS-I

and MEDS-II in the FBI Data Analysis Support Laboratory (DASL).

This paper describes the basic properties of the images and some relevant image quality 

characteristics that pertain to collection practices and the calibration and evaluation of face 

recognition technology. Table 1 provides an overview of the final contents of the MEDS-I

and MEDS-II corpus.

Table 1 – MEDS-II Dataset Overview

Dataset Subject 

Count

Submission 

Count

Image 

Count

MEDS-I 380 682 711

MEDS-II 138 535 598

MEDS-I & MEDS-II 518 1,217 1,309

All original submissions contain at least one logical Type-10 record, the record type within 

the ANSI/NIST-ITL 1-2007 file format reserved for face images and Scars, Marks, and 

Tattoos (SMT) images. The submission files were parsed into the various record types, as 

described below in Section 2. 

 

1 Watson, C. I. (2010, May 10). NIST Special Database 32 – Multiple Encounter Dataset I (MEDS-I). Retrieved 

December 13, 2010, from National Institutes of Standards and Technology: 

http://www.nist.gov/itl/iad/ig/sd32.cfm

2 American National Standard for Information Systems – Data Format for the Interchange of Fingerprint, 

Facial, and other Biometric Information – Part 1. NIST Special Publication 500-271, May, 2007. Online: 

http://fingerprint.nist.gov/standard/Approved-Std-20070427.pdf
Multiple Encounter Dataset (MEDS-II)

5

2 Data Preparation Methodology

This section describes the processes of EBTS decomposition, data normalization and 

correction, and face detection necessary to prepare this corpus.

2.1 EBTS Data Decomposition

The submission files were parsed and examined using a combination of government, 

commercial, and custom EBTS parsing and reporting tools to help verify consistent results. 

Table 2 presents a summary of tools used.

Table 2 – Tools used to parse and examine submission files

Tool License Developer Purpose

Universal Latent 

Workstation

GOTS Noblis3 Manual EBTS inspection

EFTSExtract GOTS MITRE Batch extraction and reporting

Google Picasa COTS Google Gallery viewing

PittPatt 4 COTS Pittsburgh Pattern 

Recognition

Tools for face detection

Stasm N/A S. Milborrow, F. 

Nicolls5

Annotation of face contours and 

features

MarkIt GOTS MITRE6 Face annotation and point 

editing

matplotlib PSF J. Hunter7 Data visualization

Each submission file contained an associated subject identifier to indicate the link between 

a subject and their encounters (i.e., submission files or recording events) over time. For 

many subjects in the set, more than one submission file was provided. Multiple encounters 

of individuals are sometimes referred to as recidivist encounters. The time interval between 

multiple encounters varies per individual. The cardinal relationship between subjects and

submissions and samples is shown in Figure 1.

 

3

http://www.noblis.org

4

http://pittpatt.com

5 Milborrow, S., & Nicolls, F. (2008). Locating Facial Features with an Extended Active Shape Model. ECCV , 

http://www.milbo.users.sonic.net/stasm.

6

Pruitt, M. (1, June 2010). MarkIt. McLean, VA, USA

7 Hunter, J. (2010, November 9). matplotlib Release 1.0.0. Retrieved December 13, 2010, from matplotlib: 

http://matplotlib.sourceforge.net/index.html
Multiple Encounter Dataset (MEDS-II)

6

Figure 1 – Relationship between Subjects, Submissions and Biometric Samples

After establishing ground truth for this dataset (described in Section 3), the Type-10 images 

were assessed for their face content. Table 3 summarizes the image content as observed in 

the original submission files. Not all images are face images or considered part of the MEDSII dataset.

Table 3 – Number of Type-10 Images and Submissions

Number of 

Images

Number of 

Submissions Comments

1 1,217 1st image is frontal or near frontal face image

2 72 2nd image is usually a profile face image

3 20 3rd image is usually a profile face image

2.2 Data Normalization and Correction

The consistency and reliability of the biographic data in the submissions varies, presumably 

due to input error or inconsistent information collection from subjects who may not have 

cooperated with the process. Some data normalization and corrections were performed to 

alleviate these errors on the metadata relevant to face detection and recognition (e.g., dates, 

gender, and race fields).

The date of arrest (DOA) and photo date (PHD) should be, by definition, within close date 

proximity of each other, and the PHD should always follow the DOA if the dates are not 

identical. In instances where either of these dates was missing or corrupt, the most 

repeated date among the entries was used for analysis. In the accompanying metadata file, 

an indicator is used to identify which records had been modified from their original 

contents. 

2.3 Face Detection and Pose Labeling

Executing automated face detection was the first step in distinguishing the face-containing 

images from non-face-containing images. The face-containing images were additionally 

delineated into frontal and non-frontal bins based on the PittPatt (the tool used for face 

detection) pose estimates. Human reviewers manually reviewed each category to remove 

residual errors and obtain the final ground truth for the subject’s pose.

As shown in Table 4, below, 1,219 of the images are frontal or “near frontal”, as determined 

by human review. Frontal images are defined as within 15 degrees horizontal of full frontal,

as estimated by visual inspection. Near frontal is defined as within 45 degrees horizontal, 
Multiple Encounter Dataset (MEDS-II)

7

but not overlapping with the defined yaw angle range for frontal. Profiles or “near profiles” 

are likewise within 45 degrees of full profile, although “two-eyed” near profiles may be 

closer to 45-60 degrees off full profile. These definitions are working definitions and are 

prone to human error. Images where the pose yaw angle is compounded with pitch and roll

deviations are even more prone to human review variations.

Table 4 – Image Types

Type Count Comments

Frontal 858

15,15 degrees yaw angle

Near Frontal 361

45, 15 15,45

degrees yaw angle

Near Profile 6

60, 45 45,60

degrees yaw angle

Profile 85

90, 60 60,90

degrees yaw angle

Total: 1,309

2.4 Recidivists and Match Pairs

Near profile and profile images are included in the dataset to benefit research and 

development; however, these images are omitted in the count of match pairs. After profile

and near profile images were removed, the number of match pairs is based on the 

remaining frontal or near frontal images (1,219 images). Table 5 enumerates the number of 

match pairs over the subjects based on the number of images per subject. The table only 

refers to frontal and near-frontal images.

Table 5 – Enumeration of Match Pairs

Number of 

Subjects

Number of 

Images

Number of 

Match Pairs

262 1 N/A

124 2 124

47 3 141

22 4 132

15 5 150

12 6 180

9 7 189

9 8 252

4 9 144

6 10 270

5 11 275

1 13 78

1 16 120

1 18 153

Total: 2,208
Multiple Encounter Dataset (MEDS-II)

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3 Description of Corpus

This section provides a summary of the subject metadata contained within the MEDS-II

dataset.

3.1 Race and Gender

Race and gender information are based on observation or provided by the subject. Race can 

be ambiguous and ultimately is a social or cultural interpretation (as opposed to a 

consistently defined attribute for labeling). Race and gender, as provided in the data, is 

shown in Figure 2.

Figure 2 – Distribution of Gender by Race

3.2 Age Summary and Time between Encounters

Figure 3, below, illustrates the ages of the 518 subjects at the time the images were 

captured. The age of the subjects at the time of collection is also provided in the 

accompanying metadata for this dataset.
Multiple Encounter Dataset (MEDS-II)

9

Figure 3 – Histogram of Subject Ages at Photo Date

Of the images in the dataset, 48% are of subjects between the ages of 15 and 30 years of age. 

Nine percent of the images in the dataset are of subjects greater than 50 years old while the 

oldest subject in the dataset is 69 years of age.

Figure 4 illustrates the times between the first and last encounter for all the subjects with 

multiple encounters. The horizontal axis is organized in bins of six month intervals.

Figure 4 – Times between Encounters (e.g., first and last)
Multiple Encounter Dataset (MEDS-II)

10

Of the times between encounters, 47% are less than one year. The remainders are between 

one and five years (49%) and greater than five years (4%).

3.3 Image Dimensions

Image sizes and approximate resolution of the face vary due to the use of different camera

equipment and composition inconsistencies of the subject in the image frame. Figure 5 is 

comprised of three charts, a histogram which illustrates the number of images by width, a 

histogram which illustrates the number of images by height, and a scatter plot which 

illustrates the number of images by both height and width. Of the images’ dimensions, 70%

are approximately 0.3 megapixels while one image exceeds five megapixels. The red box in 

Figure 5 identifies the dimensions of roughly 70% of all images in the corpus.

Figure 5 – Image Dimensions
Multiple Encounter Dataset (MEDS-II)

11

According to section 6 of the current specification, the ITL has no image scanning resolution 

for Type-10 records: “Facial/mugshot, SMT, and iris images rely on the total number of pixels 

scanned and transmitted and are not dependent on the specific scanning resolution used.”

3.4 Face Resolution and Subject Pose

Consistent face resolution requires consistent sizing and framing. The framing of the 

subjects in the MEDS-I and MEDS-II images varies and, in some instances, the full face is not 

visible. For the frontal and near frontal images, MITRE estimated interocular distances 

based on the outputs from automated face detection. The results presented in Figure 6 and 

Figure 7 are based on 1,219 images that have been identified as frontal or near frontal

andare based on automated outputs that were not reviewed or adjusted by human review.

Figure 6 – Distribution of Estimated Interocular Distances (in pixels)

Figure 7 – Face Roll Angle (degrees from horizontal)
Multiple Encounter Dataset (MEDS-II)

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4 Face Landmarking

4.1 Landmarking Process

Accurate landmarking of face images indicates successful localization of facial features and 

also may help with determining pose estimation and conformance. The MEDS-II images

include a set of facial landmarks output by Stasm, an automated face landmarking tool 

based on Active Shape Models (ASM). Stasm is designed to work on passport-style 

photographs or on frontal views with neutral expressions.

Although all MEDS-II images were processed using Stasm, a portion of the images required 

manual correction in cases where the Stasm points were deemed inaccurate. As with most 

computer vision techniques, Stasm's ability to locate face landmarks is not as accurate as a 

human, and will occasionally make errors. In certain circumstances, manual editing of 

Stasm points was done with a custom tool, MarkIt, developed for face landmarking. Based 

on MITRE’s empirical evidence, those images that exemplify poor lighting or extreme

subject expressions tend to contain numerous errors. Cropped images will have unusable or 

stray points. Among images that had to be annotated manually, the contour of the jaw line 

proved to be a predominantly difficult area for Stasm, particularly if the subject had a beard 

or the contour of the jaw line was of low contrast. Some low contrast images were observed 

to improve performance after the contrast was boosted; however additional analysis is 

required. Figure 8 shows examples of images with output Stasm points overlaid on the 

image.

Example of Good Stasm Output Requiring 

No Manual Editing

Example of Stasm Output Requiring 

Manual Editing

Figure 8 – Example Stasm Outputs

Stasm outputs a total of 68 points which correspond to an (x, y) pixel value in the image. 

Each point corresponds to a unique facial landmark. These points are depicted in Figure 9 

and enumerated in Table 6.
Multiple Encounter Dataset (MEDS-II)

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Figure 9 - Depiction of 68 Stasm Points

Table 6 - Listing of 68 Stasm Points

1. Right Temple 

2. Right Zygion 

3. Right Cheek Top 

4. Right Cheek Bottom 

5. Right Gonion 

6. Right Chin Top 

7. Right Chin Bottom 

8. Menton 

9. Left Chin Bottom 

10. Left Chin Top 

11. Left Gonion 

12. Left Cheek Bottom 

13. Left Cheek Top 

14. Left Zygion 

15. Left Temple 

16. Left Eyebrow Outer 

17. Left Eyebrow Outer Top 

18. Left Eyebrow Inner Top 

19. Left Eyebrow Inner 

20. Left Eyebrow Inner Bottom 

21. Left Eyebrow Outer Bottom 

22. Right Eyebrow Outer 

23. Right Eyebrow Outer Top 

24. Right Eyebrow Inner Top 

25. Right Eyebrow Inner 

26. Right Eyebrow Inner Bottom 

27. Right Eyebrow Outer Bottom 

28. Right Eye Outer 

29. Right Eye Top 

30. Right Eye Inner 

31. Right Eye Bottom 

32. Right Pupil 

33. Left Eye Outer 

34. Left Eye Top 

35. Left Eye Inner 

36. Left Eye Bottom 

37. Left Pupil 

38. Right Nasion 

39. Right Alare Crease 

40. Right Alare 

41. Right Nostril 

42. Subnasale 

43. Left Nostril 

44. Left Alare 

45. Left Alare Crease 

46. Left Nasion 

47. Right Nose Tip 

48. Left Nose Tip 

49. Right Chelion 

50. Right Lip Outer Top 

51. Right Lip Inner Top 

52. Lip Top 

53. Left Lip Inner Top 

54. Left Lip Outer Top 

55. Left Chelion 

56. Left Lip Outer Bottom 

57. Left Lip Inner Bottom 

58. Lip Bottom 

59. Right Lip Inner Bottom 

60. Right Lip Outer Bottom 

61. Right Lip Bottom Center 

62. Bottom Stomion 

63. Left Lip Bottom Center 

64. Left Lip Top Center 

65. Top Stomion 

66. Right Lip Top Center 

67. Stomion 

68. Pronasale 
Multiple Encounter Dataset (MEDS-II)

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4.2 Landmarking Results

Ninety-two percent (1,226 images) of the MEDS corpus was processed using the Stasm tool. 

In cases where the image was determined to be a profile, fingerprint, or marking (e.g., scar 

or tattoo), Stasm was not used and no landmark locations were generated. All Stasm result 

points were normalized according to the width and height of the image in pixels and 

subsequently compiled for analysis. 

Nearly eighty percent of the images processed by the Stasm tool were considered 

acceptable by human analysis. In some cases, output could not be produced due to the tool’s 

inability to detect a face. A summary of the percentage of images able to be processed by 

Stasm is shown in Figure 10.

Figure 10 - Proportion of Automated vs. Manual Landmarking

Figure 11 and Figure 12 further isolate Stasm’s ability to produce automated landmarks 

based on race and gender, respectively. MITRE postulates that the reason for performing a 

higher percentage of manual landmarking on Black or African American subjects is due to 

the lack of contrast between chin and neck in the image. Additionally, MITRE hypothesizes

that male images having beards failed automated landmarking.

Figure 11 – Ability to Automatically Landmark Based on Race

Automatic 

79%

No Output

2%

Manual

19%

0% 20% 40% 60% 80% 100%

White

Black or African American

Other (Indian, Asian, or Unknown)

Percentage of Images by Race 

Automatic

Manual
Multiple Encounter Dataset (MEDS-II)

15

Figure 12 - Ability to Automatically Landmark by Gender

A high level analysis between PittPatt’s confidence scores and yaw values, as correlated 

with Stasm output (e.g., rejection or acceptance by a human reviewer) was completed. 

Figure 13 and Figure 14 depict the correlation of confidence scores and yaw values, 

respectively, against Stasm's ability to successfully landmark an image. The impact appears 

to be minimal with little correlation between a confidence scores and yaw values. 

Figure 13 – Correlation of PittPatt Confidence Scores with Automated Landmarking

Figure 14 – Correlation of PittPatt Yaw Estimate with Automated Landmarking

0% 20% 40% 60% 80% 100%

Female

Male

Percentage of Images by Gender 

Automatic

Manual

0

5 0

100

150

200

250

300

1 2 3 4 5 6 7 8 9 1 0 1 1 1 2 1 3

Images

PittPatt Confidence Scores

Manual

Automatic

0

5 0

100

150

200

250

300

350

-3 0 -2 5 -2 0 -1 5 -1 0 -5 0 5 1 0 1 5 2 0 2 5 3 0

Images

PittPatt Yaw Value Estimates

Manual

Automatic
Multiple Encounter Dataset (MEDS-II)

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4.3 Next Steps for Landmarking and the MEDS-II Corpus

The performance and robustness of facial feature localization is relevant to advancing face 

recognition and pose conformance, and there are certainly technology advancements yet to 

be achieved in this area. Additional analysis into the Stasm failures and comparative 

analysis with other landmarking approaches could be beneficial to recognition systems. 

Also of interest is to strengthen and better understand the relationships between face 

morphology (human observable features) and features utilized by machine recognition (i.e., 

Do they correspond in known ways or are they entirely divergent?).

5 Face Recognition and Imposters

Performance of face recognizers depends heavily on the fine tuning of two parameters: the 

false alarm rate and true acceptance rate. Imposters (i.e. non-mated subjects) are subjects 

identified in face recognition that are not true subjects, whose match confidence values are 

larger than the false alarm rate. MITRE performed a study of imposters to identify “look-alikes” to highlight potentially problematic images for face recognizers.

As in the face detection study, MITRE has also used PittPatt to perform face recognition on 

the images which correspond to the 518 subjects in the corpus. As part of MITRE’s

experiment, the matcher threshold was set to 0.001% false acceptance rate to reduce the 

number of matched subjects in the results. Interestingly, there was a strong correlation 

between six non-mated subjects. Table 7 tabulates the number of hits on non-mated 

subjects and displays the images that correspond to imposters.

Table 7 – False Hits

Query 

Subject

Target 

Subject

Number

of Hits

0388 0404 5

0471 0396 3

0471 0413 4

In Table 8 one can identify a few observations that generally cause problems within face 

recognition. First, all imposters in the set are comprised of African-American males, even 

though all subjects in the set were matched against each other. Second, the areas around the 

orbital region on the face appear to be similar to the human eye. Third, the shape of the 

nose of all imposters appears to be the same shape.
Multiple Encounter Dataset (MEDS-II)

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Table 8 – Results of Queried Images

Query Image Result Images

SUBJ-0388-08-01-14.jpg SUBJ-0404-05-01-13.jpg SUBJ-0404-04-01-11.jpg

SUBJ-0388-16-01-01.jpg SUBJ-0404-03-01-10.jpg SUBJ-0404-05-01-13.jpg

SUBJ-0404-05-01-13.jpg SUBJ-0388-04-01-07.jpg SUBJ-0388-08-01-14.jpg SUBJ-0388-16-01-01.jpg

SUBJ-0471-01-01-05.jpg SUBJ-0396-08-01-11.jpg SUBJ-0413-06-01-03.jpg
Multiple Encounter Dataset (MEDS-II)

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Results of Queried Images Continued

Query Image Result Images

SUBJ-0471-03-01-01.jpg SUBJ-0396-04-01-03.jpg SUBJ-0396-07-01-05.jpg SUBJ-0413-01-01-08.jpg

SUBJ-0413-02-01-09.jpg SUBJ-0413-03-01-10.jpg
Multiple Encounter Dataset (MEDS-II)

19

Appendix A - List of Acronyms

Acronym Expansion

ANSI American National Standards Institute

ASM Active Shape Models

BCOE Biometric Center of Excellence

CW Clockwise

CCW Counter-clockwise

COTS Commercial off the Shelf

DOA Date of Arrest

DASL Data Analysis Support Laboratory

EBTS Electronic Biometric Transmission Specification

GOTS Government off the Shelf

ITL Information Technology Laboratory

MEDS Multiple Encounter Dataset

MITRE The MITRE Corporation

NCIC National Crime Information Center

NGI Next Generation Identification

NIST National Institute of Standards and Technology

PHD Photo Date

PSF Python Software Foundation

SMT Scars, Marks & Tattoos