  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.
