Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011

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 Target Operational Scenarios Visible-NIR-SWIR 320 x 256 x 225

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 A Fundamental Problem & A Solution Contrast Problem

UNCLASSIFIED Algorithmic Concept Framework

UNCLASSIFIED Proof of Principle Experiment Spectral Tracking – Frame i Pseudo-Color Target

UNCLASSIFIED Proof of Principle Experiment Spectral Tracking – Frame i+1

UNCLASSIFIED Proof of Principle Experiment Spectral Tracking – Frame i+40

UNCLASSIFIED Target

Unknown Probability Distribution Functions Contrast LWIR Hyperspectral Specific Face Detection LWIR 8-11  m 410 bands Assumptions: Range is known Facial spectral mixture is distinct 200 ft 300 ft 400 ft

200 ft Pseudo-Color 300 ft 400 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 o C) 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 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