Final Portal Mbgc Workshop2 v2

8/6/2019 Final Portal Mbgc Workshop2 v2

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05 December 2008

Portal Challenge ProblemMultiple Biometric Grand Challenge

Preliminary Results of Version 1


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Portal Challenge Goals

Develop multi-modal algorithms Iris, face, video

Robust Failure to acquire Non-ideal biometric samples

Portal technology Stand off screening Improving this class of applications


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3

Meet the Sensors.


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Meet the LG 2200

LG EOU2200

Take 3 iris images One above quality threshold Save all three

SufficientQuality

Save

MBGC Iris Acquisition System


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Meet the Portal


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Meet the Portal

HighDefinition

(HD) VideoCamera

Near Infrared

(NIR) VideoCameras


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Meet the Portal

Near Infrared (NIR) Video Sequence

120 pixelsacross iris

2k

2k


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Iris or Ocular Recognition?

Called iris recognition. But challenge problem not restricted to iris.


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Experiment:Still Iris versus NIR

Near Infrared (NIR) Video SequenceLG 2200 Still

Target Query

Matching

Similarity Score


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Meet the LG 2200 again.

LG EOU2200

Iris video sequence

LG EOU2200 Video


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Experiment:Video Iris versus NIR

Near Infrared (NIR) VideoSequence

Target Query

Matching

Similarity Score

LG 2200 Video


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Experiment:Still Face versus HD Video

Target Query

Matching

Similarity Score

High Definition (HD) Video Sequence

Face Still


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Experiment:Multiple Biometrics

Target Query

Matching

Similarity Score

Near Infrared (NIR) Still

High Definition (HD) Video

LG 2200 Video

Face Still


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Experiment:Multiple Biometrics

Target Query

Matching

Similarity Score



Participants needed to:

Process all the video sequences

Generate template

Decide on fusion strategy

Inputs are both video sequences

LG 2200 Video

Face Still

Input is iris videosequence and face still


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Quick Summary

Target Query


Still Iris versus NIR

LG 2200 VideoNear Infrared (NIR) Video Sequence

Video Iris versus NIR

High Definition (HD) Video SequenceFace Still

Still Face versusHD Video



Multiple Biometrics:Still Iris / Still Face vs. NIR & HDVideo Iris / Still Face vs. NIR & HD

LG 2200 StillFace Still


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Portal Challenge Version 1

Number of subject sessions= 14 0

Each subject 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 iris video 1 Left iris still sample ( 6 iris images, LG2200) 1 Right iris still sample ( 6 iris images, LG2200)


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List of participants

Name Legend Iris Face Fusion

Carnegie Mellon HH X

Cognitec AA X

CU_WVU_etc. KK X X X

Dalian BB X

L-1 GG X

Lockheed FF X X X

Pittsburgh Pattern II X

IUPUI MM X

SAGEM DD X X XSudParis CC X

Surrey EE X

Toshiba LL X


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Coming up:Multiple Biometrics ROC



Multiple Biometrics:Still Iris / Still Face vs. NIR & HDVideo Iris / Still Face vs. NIR & HD

LG 2200 StillFace Still


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

22Results from an Open Book Challenge Problem, NOT an Independent Evaluation

Participant DDachieves perfectperformance on thisdata set using face andiris.


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Coming up:Still Iris versus NIR ROC


Still Iris versus NIR


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Iris Only:Still Iris vs. NIR

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


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Missing and Partial Irises in NIR

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1 ) 0 4 233 v1466 .avi (Right iris)

2) 05 1 87 v2 7 8.avi (Right iris)

3 ) 05 313 v5.avi (Partial right iris)

4 ) 05 3 24 v1 0.avi (Right iris)

5) 05 367 v5.avi (Right iris)

6 ) 05 37 0v5.avi (Right iris)

7 ) 05 37 2v 1 0.avi (Partial right iris)

8) 0 4 85 1 v117 5.avi (Right iris)

9 ) 052 33 v2 9 0.avi (Right iris)

1 0) 05 39 0v5.avi (Right iris)

11 ) 0520 1 v31 5.avi (Right iris)


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Still Iris vs. NIR,Missing and Partial Irises Removed.



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Coming up:Still Face versus HD Video ROC

High Definition (HD) Video SequenceFace Still

Still Face versusHD Video


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Face Only:Still Face vs. HD Video



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Still Face vs. HD Face,Verification Rates at FAR=1/100



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Coming up:Video Iris versus NIR ROC

LG 2200 Video Near Infrared (NIR) Video Sequence

Video Iris versus NIR


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Video Iris vs. NIR



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Conclusions

Established community Cautiously optimistic about performance Score distributions differ qualitatively among

algorithms 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


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Meta-conclusions

Full range of problems and performance Perfect on multiple biometric fusion Random on activity sequences

A lot in between First characterization of iris performance onstand-off sensor data

Unconstrained video is a significant challenge Potential resolution/compression insensitivity Cautiously optimistic about portal Human recognition from video


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MBGC ver2 and MBE 2009


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MBGC version2

Scheduled release January 200 9 Portal Challenge

Increased number of subject sessions

Video Challenge Increased number of subject sessions

Still Face Additional resolution and compression rates Cross comparisons


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MBE 2009

Three tracks Portal Still Face Video

Run at NIST Sequester data Portal and Video

Executable Based on FRVT 200 6 , ICE 200 6 , and MBGC

Still face track Operational data Submission of SDKs will be an option

Summer 200 9 Staggered start of three tracks

Documents

Final Portal Mbgc Workshop2 v2