The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition

• URL: http://arxiv.org/abs/2002.12257v1
• Date: Thu, 27 Feb 2020 16:57:36 GMT
• Title: The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition
• Authors: Max Ruby, David S. Bolme, Joel Brogan, David Cornett III, Baldemar Delgado, Gavin Jager, Christi Johnson, Jose Martinez-Mendoza, Hector Santos-Villalobos, Nisha Srinivas
• Abstract summary: Face recognition in unconstrained environments poses a number of unique challenges ranging from glare, poor illumination, driver pose and motion. We further develop the hardware and software of a custom vehicle imaging system to better overcome these challenges. We name the Mertens Unrolled Network (MU-Net) for the purpose of fine-tuning the HDR output of through-windshield images.
• Score: 1.758759574398869
• License: http://creativecommons.org/licenses/by/4.0/
• 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 re-formulate 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.

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