Human Identification From a Distance

D. Adjeroh, B. Cukic, M. Gautam, L. Hornak, A. Ross

Lane Department of CSEEWest Virginia University

NC-BSI, December 2008

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Problem Statement

• Surveillance datasets acquired at border zones offer an opportunity to recognize individuals from a distance rather than requiring close visual inspection. The project will develop methods for human identification from surveillance videos.

• Methodology • Develop a hierarchical approach to human recognition from a

distance. • Define event clustering in joint biometric – surveillance space. • Search methods: from events to biometric profiles and vice

versa.

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Image Quality at a Distance

High Sensitivity to • Motion blur: because of long focal distance

• Out-of-focus Blur: because of small DOF

• Distortion due to lens

• Low pixel count: (sensor resolution is limited)

• Magnification blur (due to high magnification)

(66× , 50m) (109×, 100m) (153×, 150m) (284×, 300m)

Note: (magnification, distance) approximately the same resolution: 60 pixels between the eyes.

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Effect of Frame Resolution

2 4 6 8 1010

20

30

40

50

60

70

80

90

Rank

CM

C

35 pixels45 pixels60 pixels85 pixels

10x,52f

10x,44f

10x,31f

15x,31f

40 50 60 70 8010

20

30

40

50

60

Pixels between eyes

CM

C a

t ra

nk

1

Rank 1

CMC curves

60 pixels35 pixels

85 pixels

10x, 52f 10x, 31f 15x, 31f

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Effect of Illumination

2 4 6 8 1010

20

30

40

50

60

70

80

90

Rank

CM

C

No slide light100% roof light50% roof lightNo roof light

No side ligth 100% roof light 50% roof light No roof light10

15

20

25

30

35

40

45

50

CM

C r

an

k 1

CMCs

Rank 1

100% roof light 50% roof light No roof light

Degradations in high magnification images:• Sensor noise• Magnification blur• Motion blur • Out of focus blur• Zoom blur• Atmospheric blur• Illumination• Contrast• Resolution

Probes: 20x magnification 52 feet, 50 pixels inter-eye distance

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Soft biometric traitsSoft biometric traits

Jain et al, “Utilizing soft biometric traits for person authentication”, Proc. International Conference on Biometric Authentication (ICBA), Hong Kong, July 2004

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Human Metrology

• 2D Model– Available from video– Possible multiple views in

surveillance

• MAT Representation– Medial Axis Transform

– Less detailed, but may be adequate for required representation

A

F

E

D

CB

K

J

I

H

G

NM

L

Decorated MAT Representation (for 2D)

MAT Representation (1D)

Multiresolution MATs

2D measurements superimposed on 3D images (3D images from Allen et al, 2004)

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Extending the Application Envelope: Virtual Identities in Space/Time

• Correlate Two Surveillance Videos

http://en.wikipedia.org/wiki/Image:Londonbombing2.jpg

Between Aldgate East and Liverpool Street tube stations Between Russell Square and King's Cross tube stations

At Edgware Road tube stationOn bus at Tavistock Square

http://news.bbc.co.uk/1/hi/uk/4661059.stm

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Extending the Application Envelope (2)

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Biometric Surveillance Space

Video Sequence

Bio

met

ric

Info

rmat

ion

C11 C12 C1n

C21 C22 C2n

Cm1 Cm2 Cmn

Cik

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Decomposing a Video Stream

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Retrieval/Analysis Paradigms

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Leverage

• The Center for Identification Technology Research (NSF I/UCRC).

• Biometrics: Performance, Security and Social Impact, (NSF and DHS – Human Factors)• Biometric recognition from video streams, data

collection.

• Night time biometrics (ONR).• Video/image compression.

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Deliverables

Years 2-6:

• Architecture of the joint identity-surveillance space, • Efficient segmentation and labeling algorithms, • Fusion algorithms for identification from

surveillance video,• Storage and retrieval architecture.• System evaluation.