Portal Challenge Problem 

Multiple Biometric Multiple Biometric Grand Challe

Challe

Preliminary Results of Version 

05 D
Portal Challenge Goals 

– Develop multi-modal algorithms

• Iris, face, video• Failure to acquire• Non-ideal biometric samples

Robust 

– Portal technology

• Stand off screening• Improving Improving 

this class of applications
Meet the Sensors Meet the Sensors.... 
Meet the LG 2200 

MBGC Iris Acquisition System 

LG EOU2200 Sufficient 

Quality 

• Take 3 iris images • One above quality threshold One above quality threshold • Save all three 
Meet the Portal 
Meet the Portal 

High 

Definition 

(HD) Video 

Camera 

Near Infrared 

(NIR) Video 

Cameras 
Meet the Portal 

120 pixels 

across iris 

2k 

2k 

Near Infrared (NIR) Video Se
Iris or Ocular Recognition? 

• Called iris Called iris recognition recognition.• But challenge problem not restricted to 
Target Query 

LG 2200 Still Near Infrared (NIR) Vide

Matching 

Similarity Score 

Experiment: Still Iris versusNIR
Note: Input is a full vide

Similarity Score 

Target Query 

Near Infrared (NIR

Sequence 

LG 2200 Still 

Matching 

• Participants need

• Process the v

• Locate the iri• Segment the • Generate tem

Experiment: Still Iris versus NIR versus NIR 
Meet the LG 2200 again.... 

Iris video seq uence 

LG EOU2200 LG EOU2200 Video 
Target Query 

Near Infrared (NIR

Sequence 

Matching 

LG 2200 Video 

Similarity Score 

Experiment: Video Iris versus N versus NIR
Similarity Score

Sequence 

• Segment the irises• Generate temp late

Target Query 

Near Infrared (NIR

Matching 

LG 2200 Video 

• Participants needed to:

• Process the video

• Locate the irises

Experiment: Video Iris versus N versus NIR

Both inputs are full video sequences
Meet the Portal again....

High Definition (HD) Video 
Experiment: 

Still Face versus HD Video 

Target Query 

Matching 

High Definition (HD) Video 

Face Still 

Similarity Score 
Experiment: 

Multiple Biometrics 

Target Query 

Near Infrared 

LG 2200 Video 

Matching 

High Definition 

Face Still 

Similarity Score 
Similarity Score

Experiment: Multiple Biometrics 

Input is iris video 

sequence and face still Inputs are both vide

• Participants needed to:

• P i rocess all th e vid eo sequences

• Generate template

• Decide on fusion strategy 

Target Query 

• Decide on fusion strategy 

Near Infrared 

Matching 

High Definition 

LG 2200 Vid

Face Still 

LG 2200 Video 
Quick Summary 

LG 2200 Still Near Infrared (NIR) Vide

Still Iris versus NIR 

Video Iris versus NIR 

LG 2200 Video Near Infrared (NIR) Vide

Target Query 

Multiple Biometrics: Near Infrared Still Iris / Still Face vs. NIR & HD Video Iris / Still Face vs. NIR & HD

High Definition 

LG 2200 Still Face Still 

High Definition (HD) Vid

Face Still 

Still Face versus 

HD Video 
Portal Challenge Version 1 

• Number of subject sessions subject sessions= 140 • Each subjec t session consists of 

– 1 NIR (from Iris On the Move) – 1 High definition video – 1 Still face (controlled illumination) 

– 1 Left iris video 

– 1 Right 1 Right iris video – 1 Left iris still sample (6 iris images, LG22– 1 Right iris still sample (6 iris images, LG2
Name Legend Iris Face 

Carnegie Mellon Carnegie Mellon HH X Cognitec AA X 

CU_WVU_etc. KK X X 

Dalian BB X 

L-1 GG X Lockheed FF X X 

Pittsburgh Pattern II X 

IUPUI MM X SAGEM DD X X 

SudParis CC X 

Surrey EE X 

Toshiba LL X 

List of participants 
Coming up:

Multiple Biometrics ROC 

Multiple Biometrics: Near Infrared (Still Iris / Still Face vs. NIR & HD Vid I i / Still F NIR & HD 

High Definition (

Video Iris / Still Face vs. NIR & HD 

LG 2200 Still Face Still 
Participant DD 

achieves perfectperformance on this data set using face and iris. 

Multiple Biometrics: Still Iris / Still Face vs. NIR / HD vs. NIR / HD Fa

Results from an Open Book Challenge Problem, NOT an Independent Evaluation 
Coming up:

Still Iris versus NIR ROC 

LG 2200 Still N I f d (NIR) Vid 

Still Iris versus NIR 

LG 2200 Still Near Infrared (NIR) Video 
Iris Only: 

Still Iris vs. NIR vs. NIR 

Results from an Open Book Challenge Problem, NOT an Independent Evaluation 
Missing and Partial Irises in NIR 

1) 04233v1466.avi (Right iris)2) 05187v278.avi (Right iris)3)

0 

5313 v5.avi (P artila rih gt iri ) s 

4) 05324v10.avi (Right iris) 5) 05367 5 i 5) 05367v5.avi (Ri hgt i i )rs 6) 05370v5.avi (Right iris)7) 05372v10 avi 05372v10.avi (Partial right (Partial right iris) 8) 04851v1175.avi (Right iris)

9) 05233v290 05233v290 avi . (Right iris) 

10) 05390v5.avi (Right iris) 11) 05201v315.avi 05201v315.avi (Right iris) 
Still Iris vs. NIR, 

Missing and Partial Irises Removed. 

Results from an Open Book Challenge Problem, NOT an Independent Evaluation 
Coming up: Coming up: 

Still Face versus HD Video ROC 

High Definition (HD) Video 

Definition Video

Still Face versus 

HD Video 

High (HD) 

Face Still 
Face Only: 

Still Face vs. HD Video

Results from an Open Book Challenge Problem, NOT an Independent Evaluation 
Still Face vs. HD Face, 

Verification Verification Rates at FAR 1/100 =

Results from an Open Book Challenge Problem, NOT an Independent Evaluatio
Coming up:

Video Iris versus NIR ROC 

LG 2200 Video Near Infrared (NIR) Video 

Video Iris versus NIR 
Video Iris vs. NIR 

Results from an Open Book Challenge Problem, NOT an Independent Evaluation 
Conclusions 

• Established community• Cautiously optimistic about performanc

performanc

• Score distributions differ qualitatively a

algorithmsalgorithms

• Performance on portal iris

– First generation algorithms promising

• Performance on portal face

– Can be near perfect on limited data set

• Need larger data set

– Better understanding of face performance

– Allows meaningful fusion experiments experiments
Meta-conclusions 

• Full range of problems problems and performanc

performanc

– Perfect on multiple biometric fusion– Random on activity sequences– A lot in between

• First characterization of iris performanc

stand

-off sensor data

• Unconstrained video is a significant ch• Potential Potential resolution/compression resolution/compression insen

• Cautiously optimistic about portal• Human recognition from video
MBGC ver2 and MBE 2
MBGC version2 

• Scheduled Scheduled release January 2009 release January 

• Portal Challenge

– Increased Increased number of subject sessions sessions

• Video Challenge

– Increased Increased number of subject sessions sessions

• Still Face

– Additional resolution and compression rate

– Cross comparisons
MBE 2009 

• Three tracks 

– Portal– Still F ace 

– Video 

• Run at NIST • Sequester data • Portal and Video 

– Executable – Based on FRVT 2006, ICE 2006, and MBGC 

• Still face track 

– Operational data 

– Submission of SDKs will be an option 

• Summer 2009 

– Staggered start of three tracks