Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE...
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![Page 1: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/1.jpg)
Spectral LWIR Imaging for Remote
Face Detection
Dalton RosarioU.S. Army Research Laboratory
IEEE IGARSS, Vancouver, Canada29 July 2011
![Page 2: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/2.jpg)
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
![Page 3: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/3.jpg)
UNCLASSIFIED
UNCLASSIFIED
Target
Operational Scenarios
Visible-NIR-SWIR 320 x 256 x 225
![Page 4: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/4.jpg)
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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
![Page 5: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/5.jpg)
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A Fundamental Problem & A Solution
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![Page 6: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/6.jpg)
UNCLASSIFIED
UNCLASSIFIED
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Algorithmic Concept Framework
![Page 7: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/7.jpg)
UNCLASSIFIED
UNCLASSIFIED
Proof of Principle ExperimentSpectral Tracking – Frame i
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![Page 8: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/8.jpg)
UNCLASSIFIED
UNCLASSIFIED
Proof of Principle ExperimentSpectral Tracking – Frame i+1
![Page 9: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/9.jpg)
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UNCLASSIFIED
Proof of Principle ExperimentSpectral Tracking – Frame i+40
![Page 10: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/10.jpg)
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Target
![Page 11: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/11.jpg)
Unknown Probability Distribution Functions
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LWIR Hyperspectral Specific Face Detection
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Assumptions: • Range is known• Facial spectral mixture is distinct
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![Page 12: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/12.jpg)
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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
![Page 13: Spectral LWIR Imaging for Remote Face Detection Dalton Rosario U.S. Army Research Laboratory IEEE IGARSS, Vancouver, Canada 29 July 2011.](https://reader035.fdocuments.in/reader035/viewer/2022062805/5697bff21a28abf838cbbad3/html5/thumbnails/13.jpg)
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