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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 RCNN for face detection and GAN for machine learning-inspired High Dynamic Range Imaging, artifact removal, and image fusion.

Authors:
 [1]; ORCiD logo [1];  [1];  [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1559726
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: IS&T International Symposium on Electronic Imaging (EI 2019) - San Fransisco, California, United States of America - 1/13/2019 3:00:00 PM-1/17/2019 10:00:00 AM
Country of Publication:
United States
Language:
English

Citation Formats

Cornett Iii, David, Yen, Alec, Noyola, Grace, Montez, Diane, Johnson, Christi, Baird, Seth T., Santos-Villalobos, Hector, and Bolme, David. Through the Windshield Driver Recognition. United States: N. p., 2019. Web.
Cornett Iii, David, Yen, Alec, Noyola, Grace, Montez, Diane, Johnson, Christi, Baird, Seth T., Santos-Villalobos, Hector, & Bolme, David. Through the Windshield Driver Recognition. United States.
Cornett Iii, David, Yen, Alec, Noyola, Grace, Montez, Diane, Johnson, Christi, Baird, Seth T., Santos-Villalobos, Hector, and Bolme, David. Tue . "Through the Windshield Driver Recognition". United States. https://www.osti.gov/servlets/purl/1559726.
@article{osti_1559726,
title = {Through the Windshield Driver Recognition},
author = {Cornett Iii, David and Yen, Alec and Noyola, Grace and Montez, Diane and Johnson, Christi and Baird, Seth T. and Santos-Villalobos, Hector and Bolme, David},
abstractNote = {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 RCNN for face detection and GAN for machine learning-inspired High Dynamic Range Imaging, artifact removal, and image fusion.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

Conference:
Other availability
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