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Spectral LWIR Imaging for Remote Face Detection. Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011. Outline. Unrelated Operational Concept A Difficult Target Detection Problem Proposed Algorithmic Framework Experimental Results - PowerPoint PPT Presentation
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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
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Algorithmic Concept Framework
UNCLASSIFIED
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Proof of Principle ExperimentSpectral Tracking – Frame i
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UNCLASSIFIED
UNCLASSIFIED
Proof of Principle ExperimentSpectral Tracking – Frame i+1
UNCLASSIFIED
UNCLASSIFIED
Proof of Principle ExperimentSpectral Tracking – Frame i+40
UNCLASSIFIED
UNCLASSIFIED
Target
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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
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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