Spectral LWIR Imaging for Remote Face Detection
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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
description
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
Transcript of Spectral LWIR Imaging for Remote Face Detection
Spectral LWIR Imaging for Remote
Face Detection
Dalton RosarioU.S. Army Research Laboratory
IEEE IGARSS, Vancouver, Canada29 July 2011
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• Unrelated Operational Concept• A Difficult Target Detection Problem• Proposed Algorithmic Framework• Experimental Results• Adaptation to LWIR Specific-Face Detection• Experimental Results• Concluding Remarks
Outline
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Target
Operational Scenarios
Visible-NIR-SWIR 320 x 256 x 225
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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
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A Fundamental Problem & A Solution
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Algorithmic Concept Framework
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Proof of Principle ExperimentSpectral Tracking – Frame i
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Proof of Principle ExperimentSpectral Tracking – Frame i+1
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Proof of Principle ExperimentSpectral Tracking – Frame i+40
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LWIR Hyperspectral Specific Face Detection
LWIR8-11 m
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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
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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
