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 continue the development of a camera system to overcome these challenges. Using this system we collect a small dataset of through-windshield image captures of known drivers. We then formulate the classical Mertens-Kautz-Van Reeth HDR imaging method as a neural network, called the Mertens Unrolled Network (MU-Net), for the purpose of High Dynamic (HDR) fusion of these images. This novel HDR method is then evaluated as the input to a state-of-the-art facial recognition network and compared against the performance of traditional HDR methods and a previously developed GAN method for HDR image correction.