Through the windshield driver recognition

David Cornett; Alec Yen; Grace Nayola; Diane Montez; Christi R. Johnson; Seth T. Baird; Hector Santos-Villalobos; David S. Bolme

doi:10.2352/ISSN.2470-1173.2019.13.COIMG-140

Through the windshield driver recognition

David Cornett, Alec Yen, Grace Nayola, Diane Montez, Christi R. Johnson, Seth T. Baird, Hector Santos-Villalobos, David S. Bolme

Research output: Contribution to journal › Conference article › peer-review

4 Scopus citations

Abstract

Biometric recognition of vehicle occupants in unconstrained environments is rife with a host of challenges. In particular, the complications arising from imaging through vehicle windshields provide a significant hurdle. Distance to target, glare, poor lighting, head pose of occupants, and speed of vehicle are some of the challenges. We explore the construction of a multi-unit computational camera system to mitigate these challenges in order to obtain accurate and consistent face recognition results. This paper documents the hardware components and software design of the computational imaging system. Also, we document the use of Region-based Convolutional Neural Network (RCNN) for face detection and Generative Adversarial Network (GAN) for machine learning-inspired High Dynamic Range Imaging, artifact removal, and image fusion.

Original language	English
Article number	COIMG-140
Journal	IS and T International Symposium on Electronic Imaging Science and Technology
Volume	2019
Issue number	13
DOIs	https://doi.org/10.2352/ISSN.2470-1173.2019.13.COIMG-140
State	Published - Jan 13 2019
Event	17th Computational Imaging Conference, CI 2019 - Burlingame, United States Duration: Jan 13 2019 → Jan 17 2019

Funding

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research was supported in part by an appointment to the Oak Ridge National Laboratory Post-Bachelors Research Associate Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education, the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI), and the Texas A&M University-Kingsville, Office of University Programs, Science and Technology Directorate, Department of Homeland Security Grant Award # 2012-ST -062-000054. This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research was supported in part by an appointment to the Oak Ridge National Laboratory Post-Bachelors Research Associate Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education, the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internships Program (SULI), and the Texas A&M University-Kingsville, Office of University Programs, Science and Technology Directorate, Department of Homeland Security Grant Award # 2012-ST -062-000054.

Funders	Funder number
DOE Public Access Plan
Office of University Programs, Science and Technology Directorate
Office of Workforce Development for Teachers
US Department of Energy
U.S. Department of Energy
U.S. Department of Homeland Security	2012-ST -062-000054
Office of Science
Workforce Development for Teachers and Scientists
Oak Ridge Institute for Science and Education
Texas A and M University-Kingsville

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10.2352/ISSN.2470-1173.2019.13.COIMG-140

Cite this

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title = "Through the windshield driver recognition",

abstract = "Biometric recognition of vehicle occupants in unconstrained environments is rife with a host of challenges. In particular, the complications arising from imaging through vehicle windshields provide a significant hurdle. Distance to target, glare, poor lighting, head pose of occupants, and speed of vehicle are some of the challenges. We explore the construction of a multi-unit computational camera system to mitigate these challenges in order to obtain accurate and consistent face recognition results. This paper documents the hardware components and software design of the computational imaging system. Also, we document the use of Region-based Convolutional Neural Network (RCNN) for face detection and Generative Adversarial Network (GAN) for machine learning-inspired High Dynamic Range Imaging, artifact removal, and image fusion.",

author = "David Cornett and Alec Yen and Grace Nayola and Diane Montez and Johnson, {Christi R.} and Baird, {Seth T.} and Hector Santos-Villalobos and Bolme, {David S.}",

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AU - Cornett, David

AU - Yen, Alec

AU - Nayola, Grace

AU - Montez, Diane

AU - Johnson, Christi R.

AU - Baird, Seth T.

AU - Santos-Villalobos, Hector

AU - Bolme, David S.

PY - 2019/1/13

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AB - Biometric recognition of vehicle occupants in unconstrained environments is rife with a host of challenges. In particular, the complications arising from imaging through vehicle windshields provide a significant hurdle. Distance to target, glare, poor lighting, head pose of occupants, and speed of vehicle are some of the challenges. We explore the construction of a multi-unit computational camera system to mitigate these challenges in order to obtain accurate and consistent face recognition results. This paper documents the hardware components and software design of the computational imaging system. Also, we document the use of Region-based Convolutional Neural Network (RCNN) for face detection and Generative Adversarial Network (GAN) for machine learning-inspired High Dynamic Range Imaging, artifact removal, and image fusion.

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DO - 10.2352/ISSN.2470-1173.2019.13.COIMG-140

M3 - Conference article

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Through the windshield driver recognition

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