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23 April 2020 The Mertens Unrolled Network (MU-Net): a high dynamic range fusion neural network for through the windshield driver recognition
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Abstract
Face recognition of vehicle occupants through windshields in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion blur. In this paper, we further develop the hardware and software components of a custom vehicle imaging system to better overcome these challenges. After the build out of a physical prototype system that performs High Dynamic Range (HDR) imaging, we collect a small dataset of through-windshield image captures of known drivers. We then reformulate the classical Mertens-Kautz-Van Reeth HDR fusion algorithm as a pre-initialized neural network, which we name the Mertens Unrolled Network (MU-Net), for the purpose of fine-tuning the HDR output of through-windshield images. Reconstructed faces from this novel HDR method are then evaluated and compared against other traditional and experimental HDR methods in a pre-trained state-of-the-art (SOTA) facial recognition pipeline, verifying the efficacy of our approach.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Max Ruby, David S. Bolme, Joel Brogan, David Cornett III, Baldemar Delgado, Gavin Jager, Christi Johnson, Jose Martinez-Mendoza, Hector Santos-Villalobos, and Nisha Srinivas "The Mertens Unrolled Network (MU-Net): a high dynamic range fusion neural network for through the windshield driver recognition", Proc. SPIE 11415, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2020, 114150B (23 April 2020); https://doi.org/10.1117/12.2566765
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