The Mertens Unrolled Network (MU-Net): A high dynamic range fusion neural network for through the windshield driver recognition

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.

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

“…Cornett et al explored the construction of a multi-unit computational camera system to get consistent face recognition results [90,91]. The system performs HDR (High Dynamic Range) imagining to create a dataset of through-windshield images.…”