WVU Biometric Data 

Collection Projects

Jeremy M. Dawson

West Virginia University

Statler College of Engineering and Mineral Resources

Lane Dept. of Computer Science and Electrical Engineering

The results presented herein were generated by work performed under FBI contract

numbers POA8A806585, POA9A906229, POA2A201589, DJF-13-1200-A-0000651,

POA1A103721, POA2A201564, DJF-13-1200-A-0000625, and DJF-14-1200-A-1115904, ONR

contract numbers N00014-12-1-0931 and N00014-08-1-0895, ManTech contract numbers

25922 (2010-IJ-CX-K024) and MASI-14-WVURC-F-828-29156, DHS contract number IIP0641331 DOJ contract number 2010-DD-BX-0161, as well as awards from the Center for

Identification Technology Research (CITeR).
Outline

• Why do we collect 

biometric data?

• Create test dataset

• Sensor evaluation/

Interoperability

• Human Factors

• Explore new modalities

• Common items associated 

with collection 

preparation

• IRB: things-to-know

• Worker training

• Dataset utility & longevity

• Lab vs. operational 

environment

• Sensor suite selection

• Data storage/management
Overview

Since 2008, WVU has performed large, medium, and small 

scale biometric data collection projects to accomplish the 

following goals:

• Build research datasets to train humans, algorithms, and 

systems

• Evaluate prototype sensor operation

• Data interoperability (e.g. contactless vs. contactbased fingerprint sensors)

• Human factors

• Explore the application of new modalities/methods

• Short-wave infrared (SWIR) imagers for cross-spectral 

facial identification

• Biometrics in difficult environments

• Bimolecular biometrics
FBI Collections – Test Datasets

Lab Collections:

• 2008-Present; collected to:

• “Build robust dataset for future 

applied research efforts, including 

prototype device and algorithm 

development”

• “Develop training materials, and in 

proficiency testing and competency 

testing”

• Primarily face (stills & video), 

fingerprint, and iris

• 2008: large latent collection (10-print, 

palm, major case, latent impressions)

• 2009: added non-ideal face 

(expressions, digital disguise), archival 

photos

• 2012: added hand geometry, ‘eyes 

closed’ face images, emphasis on 

repeat visit 1-2 months later

• 2013: added unscripted voice, audio 

booth, SWIR face

• Total of 4532 datasets, 550 repeat 

visits and counting

Facial Hair Before After Removal

Facial Hair Before After Addition
FBI Collections – Test Datasets

‘Twins Days’ Collections:

• 2010-Present; collected to:

• “Build robust dataset for future 

applied research efforts, including 

prototype device and algorithm 

development”

• “Develop training materials, and 

in proficiency testing and 

competency testing”

• Limited area (10’x10’ tents), 

limited power

• Environmental factors: heat, 

rain, sun angle

• Primarily face (stills & video), 

fingerprint, and iris

• 2013 added twin audio collection

• Total of 1736 datasets, 197 

repeat visits and counting
FBI Collections – Test Datasets

Demographic Variance:

2012 Lab 2014 Twins

12.3%

71.3%

8.8%

2.5%

3.4%

1.3%

0.5%

Participants by Age Group (%)

18 - 19 years old

20 - 29 years old

30 - 39 years old

40 - 49 years old

50 - 59 years old

60 - 69 years old

70 - 79 years old

44%

9.8%

9.9%

6.2%

1.8%

5.1%

0.3%

4.7% 0.3%1.0%

0.2%

Participants by Ethnicity Group (%)

Caucasian

Asian

Asian Indian

African American

African

Middle Eastern

Native American

Hispanic

Pacific Islander

Other

Unknown

6.8%

31.2%

17.6%

13.9%

14.2%

11.5%

3.7%

0.7%

0.3%

Participants by Age Group (%)

18 - 19 years old

20 - 29 years old

30 - 39 years old

40 - 49 years old

50 - 59 years old

60 - 69 years old

70 - 79 years old

80 - 89 years old

90 - 99 years old

44%

1.7%

1.0%

13.6%

0.3% 2.7%

Participants by Ethnicity Group (%)

Caucasian

Asian

Asian Indian

African American

Middle Eastern

Hispanic
DOJ & DHS Collections – Sensor Interoperability 

& Human Factors

3D & Contactless Fingerprints:

• 2010 DHS Collection – Goal: Evaluate 

data collected from two prototype noncontact fingerprint capture systems 

• Sensors: Flashscan3D single finger and 

GE 4-finger phase I prototypes

• Ground truth: Crossmatch Guardian, 10-

print cards

• 122 participants, 19 repeats

• 2012 & 2015 ManTech/DOJ Collection –

Goal: Evaluate data interoperability and 

perform qualitative assessment of 

operation

• Sensors 2012: Crossmatch Guardian R2, 

Crossmatch SEEK II, i3 DigID Mini, L1 

Touchprint 5300, TBS 3D-Enroll 

(commercial; Series 11), FlashScan3D D1 

single-finger (V2), FlashScan3D D4 fourfinger (V1)

• Sensors 2015: Crossmatch Guardian R2, 

Crossmatch SEEK Avenger, NG BioSled, 

Moprho Ident, Morpho Finger-on-the-Fly, 

ANDI On-the-Go, Flashscan D1 

(production), IDAir InnerID (iPhone app) 

• Ground truth: 10-print cards (scanned)

• 500 participants for 2012, 400 planned for 

2015

L. Lugini, E. Marasco, B. Cukic, and J. Dawson, “Removing Gender Signature from Fingerprints,” in 

Proc. Biometrics & Forensics & De-identification and Privacy Protection (BiForD), May 2014, Croatia.
DOJ & DHS Collections – Sensor Interoperability 

& Human Factors

3D & Contactless Fingerprints:
DOJ & DHS Collections – Sensor Interoperability 

& Human Factors

Long-Range 3D Face:

• 2012 & 2013 ManTech/DOJ Collection –

Goal: Evaluate data interoperability and 

perform qualitative assessment of 

operation

• 2012 Sensors: Stereovision binoculars 

prototype (V1), Sony DEV 5 digital 

recording binoculars

• 2013 Stereovision binoculars prototype 

(V2)

• Ground Truth: Digital SLR camera

• Outdoors: Canon 5D MkII digital SLR camera with a 

Canon EF 800mm f/5.6L IS USM Autofocus Lens

• Indoors: Canon 5D Mk II digital SLR camera with a 

Canon EF 70-200mm (f/2.8, image stabilized) lens, 

standard 5-pose mugshots

• 100 participants each, 2012 & 2013
ONR Collections – SWIR Biometrics

2011-2013 Face in Challenging Environments

• Goal: Develop algorithms for cross-spectral 

face matching at night and obstructed by 

tinted materials

• SWIR imager, active (1150nm laser source), 

tungsten, and natural illumination

• 1050-1650nm wavelengths, filtered at 100nm bands

• Phase I: Indoor collection under varying 

lighting conditions

• 138 participants

• Phase II: Outdoors collection under 

environmental lighting, both day and night

• 200 participants

J. Ice, N. Narang, C. Whitelam, N. Kalka, L. Hornak, J. Dawson, and T. Bourlai, “SWIR 

Imaging for Facial Image Capture Through Tinted Materials,” Proc. SPIE, 8353, p. 83530S, 

2012.
ONR Collections – SWIR Biometrics

2011-2013 Face in 

Challenging Environments

Variations in Image Quality with Varying Collection 

Conditions (all images @ 1550nm)

Sample Daytime Images

Sample Indoor Images
ONR Collections – SWIR Biometrics

2013 Long-Range SWIR Face • Performed in partnership with 

WVHTCF (Fairmont, WV) using 

TINDERS imager

• SWIR images captured at 100, 200, 

& 350 meters

• Faces captured behind tinted glass 

at each location

• 104 participants
ONR Collections – SWIR Biometrics

2011 Gait & Body Measurements

50m 40m 30m 20m 0m

1

2 3

4 5

6

35m

8 7

Camera

B. DeCann, A. Ross, and J.M. Dawson, “Investigating gait recognition in the short-wave infrared (SWIR) 

spectrum: dataset and challenges,” Proc. SPIE 8712, Biometric and Surveillance Technology for Human 

and Activity Identification X, 87120J, May 31, 2013.

• Gait video captured with MS Kinect (indoors, 

short range) and SWIR camera (outdoors, long 

range)

• Body measurements recorded as well

• 157 participants
CITeR/DOJ Bimolecular Biometrics

DNA & Face Images

• 5-pose face images and blood samples

• 250 participants

• 20 sequenced genomes

Hand Bacteria

• Hand swabs from right/left hands

• 250 participants

• 56 samples isolated and sequenced (16s rRNA)

Touch DNA & Latent Fingerprints

• Latent impression on plastic

• Touch DNA recovered from fingerprints

• 35 participants

NetBio Instrument Validation

• 5 minute buccal swab; performed in high-traffic areas on campus

• Two 2-day collections; 600 collected first collection, 200 second 

collection

A.B. Holbert, H.P. Whitelam, L.J. Sooter, J.M. Dawson, and L.A. Hornak, “Evaluation of Hand Bacteria as a Human 

Biometric Identifier,” in Proc. IEEE 14th International Conference on BioInformatics and BioEngineering, pp. 83-89, 

November 10-12, Boca Raton, FL (2014).
Outline

• Why do we collect 

biometric data?

• Create test dataset

• Explore new modalities

• Sensor evaluation/

Interoperability

• Human Factors

• Common items associated 

with collection 

preparation

• IRB: things-to-know

• Worker training

• Dataset utility & longevity

• Lab vs. operational 

environment

• Sensor suite selection

• Data storage/management
IRB Protocol Review – Things to Know

• Most biometric collections are considered minimal risk 

studies, however...

• Prototype ‘devices’ necessitate full-board review, 

inclusion of safety documentation in protocol 

(typically exempt from FDA certification since 

assembly of COTS components)

• Human DNA collection may require additional 

biosafety protocol(s), necessitate full board review

• If planned, data release or sharing needs to be 

explained clearly in consent form

• Collection of physical metadata (height, weight, etc.) 

does not require HIPAA forms if not correlating to 

participant health
Worker Training

• Easy-to-use sensor interfaces crucial to data consistency 

• Standard operating procedures essential
Outline

• Why do we collect 

biometric data?

• Create test dataset

• Explore new modalities

• Sensor evaluation/

Interoperability

• Human Factors

• Common items associated 

with collection preparation

• IRB: things-to-know

• Worker training

• Dataset utility & longevity

• Lab vs. operational 

environment

• Sensor suite selection

• Data storage/management
Lab vs. Operational Conditions

• Laboratory settings allow for control over common variables 

impacting data quality (lighting, presentation, etc.)

• Sometimes results in data that is “too good”

• Some quality variance due to sensor variance, operator habits; helpful to 

track operator/station IDs

• Operational conditions pose challenges to algorithms developed 

solely on lab data

• Distance, environmental factors (darkness, weather), lack of enrollment 

opportunity, subject cooperation, collection speed, etc.

Empire Challenge 2010 WVU Face Recognition Identified ‘Pakol’ at a 

distance of 400 meters (Imaged by WVHTCF TINDERS)

Drastic change in 

grass height over 

course of exercise; 

occluded key 

features needed for 

gait recognition
Sensor Selection

• Sensor technology continually 

improving/updated

• Legacy data may still see widespread use

• Necessitates co-collection of data from new and 

old devices

X

X Sarnoff IOM –

High-res Face 

+ Iris

Integrated 

Biometrics 

Sherlock – TFT 

technology
Data Storage & Management

• Data storage needs can grow quickly

• 2009 FBI collection – 1.2TB

• 2012 FBI collection – 3.5TB

• 2013 FBI Collection - ??? Audio files are 5GB per participant

• Should data be kept 

indefinitely?

• Sensors may no longer be 

relevant

• IRB may require limits on 

longevity

• Does your data require a 

release policy?

• IRB may require release plan 

if data will be shared

• Staff may be needed to 

maintain release requests
Thank You!

QUESTIONS?

• FBI datasets are available upon request; contact Joey Newell 

(Joey.Newell@ic.fbi.gov)

• ONR SWIR data availability is contingent upon sponsor approval after project 

conclusion (2015)

• Other dataset inquiries can be directed to Jeremy.Dawson@mail.wvu.edu