Spectral LWIR Imaging for Remote

Face Detection

Dalton RosarioU.S. Army Research Laboratory

IEEE IGARSS, Vancouver, Canada29 July 2011

UNCLASSIFIED

UNCLASSIFIED

• Unrelated Operational Concept• A Difficult Target Detection Problem• Proposed Algorithmic Framework• Experimental Results• Adaptation to LWIR Specific-Face Detection• Experimental Results• Concluding Remarks

Outline

UNCLASSIFIED

UNCLASSIFIED

Target

Operational Scenarios

Visible-NIR-SWIR 320 x 256 x 225

UNCLASSIFIED

UNCLASSIFIED

Non-kinematic based target detection/ tracking• Advantages Using Hyperspectral Imagery

– No geo-rectification required – No frame-to-frame registration required– Target detection (moving or stationary)– Handles challenges in kinematic based methods

• Challenge• Subset of Curse of Dimensionality Problem• Atmospheric variation, geometry of illumination, etc

Kinematic based methods– Challenges

• Changes in velocity• Proximity to other vehicles• Prolonged obscuration

Some Comments

UNCLASSIFIED

UNCLASSIFIED

A Fundamental Problem & A Solution

))(1(

)2)(1(

)1)(1(

NiframeBn

iframeBn

iframeBn

Z

Z

Z

)( iframeBn

XContrast

ContrastR

r

riframeBn 1

))(1(

X

BnProblemProblem

)( iframemN

Y))(1( riframe

mN

Y mN

)SWIR NIR, V,(3.,. mge

1 iFrameiFrame kiFrame

UNCLASSIFIED

UNCLASSIFIED

Bn

referenceBn

)(X

)(

)2(

)1(

NBn

Bn

Bn

Z

Z

Z

)(testBn

X

)(

)2(

)1(

NBn

Bn

Bn

Z

Z

Z

mN

referencemN

)(Y )(testmN

Y

)()(),( testmN

referencemN

testreferencemN YYΔ

N

b

btestreftestref N1

))(,(1),( ΔΔ ttestreftestref

mNtestreftestref

mNtestrefmm N ),(),(),(),(1),( 1 ΔΔΔΔΣ

0δ

0δ

Δ

Δ

),(

),(

:

:

1

0

testref

testref

H

H

if Reject 0H

mNm

ttestreftestrefmm

testreftestref

FmN

mN

NT

,

),(1),(),(),(2

1

δΔΣδΔ

Algorithmic Concept Framework

UNCLASSIFIED

UNCLASSIFIED

Proof of Principle ExperimentSpectral Tracking – Frame i

mNm

mN

mN F

,

1

Pseudo-ColorTargetTarget mNm

F,

FT 2

:0

0δH 0δ:1H

FT 2

05.0 yprobabiliterror

UNCLASSIFIED

UNCLASSIFIED

Proof of Principle ExperimentSpectral Tracking – Frame i+1

UNCLASSIFIED

UNCLASSIFIED

Proof of Principle ExperimentSpectral Tracking – Frame i+40

UNCLASSIFIED

UNCLASSIFIED

Target

Unknown Probability Distribution Functions

Contrast

))((

)2)((

)1)((

NkiframeBn

kiframeBn

kiframeBn

Z

Z

Z

Contrast)()( BniframeBn

X

LWIR Hyperspectral Specific Face Detection

LWIR8-11 m

410 bands

Assumptions: • Range is known• Facial spectral mixture is distinct

200 ft 300 ft 400 ft

Image of Size

1

))((

r

rkiframeBn

X

)()( mNiframemN

Y ))(( rkiframemN

Y

200 ft

mNm

mN

mN F

,

1

Pseudo-Color mNmF

,

FT 2

:0

0δH 0δ:1H

FT 2

05.0 yprobabiliterror

300 ft400 ft

Target Algorithm Suite First Level of Detection• Temperature & Emissivity Separation.• Use human body biometrics for Skin detection

• Uniform Temperature (35.5 to 37.5 oC)• IR Emissivity relatively uniform among different skin

Second Level – Specific Face Detection• Apply All bands Statistical Hypothesis Test Afterward

LWIR Hyperspectral Specific Face Detection

UNCLASSIFIED/FOUO

UNCLASSIFIED/FOUO

Concluding Remarks

• Introduced an algorithmic framework for extremely small sample size multivariate target detection problems (n << B)

• Approach is Flexible, Adaptive

• Approach Addresses Fusion of Spectral Regions

– Visible, NIR, SWIR, MWIR, LWIR

• Proof of principle experimentation for LWIR Specific-Human-Face Detection– First Level Detection: Human skin biometrics

(temperature & emissivity ranges)– Second Level – Proposed approach using All Bands on

candidate regions from first level