- By:
- Ruby, Max ; Bolme, David S; Brogan, Joel R; Cornett, David C; Delgado, Baldemar ; Jager, Gavin F; Johnson, Christi R; Martinez-Mendoza, Jose A; Santos-Villalobos, Hector J; Srinivas, Nisha
- Journal Name:
- Proceedings of SPIE
- Page Number:
- 18
- Volume:
- 11415
- Issue Number:
- N/A
- Publication Date:
- November 9, 2023
- Conference Name:
- Autonomous Systems: Sensors, Processing and Security for Vehicles & Infrastructure 2020
- Conference Location:
- Anaheim, California, United States of America
- Conference Sponsor:
- SPIE
- View DOI Listing:
- https://doi.org/10.1117/12.2566765
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.
