论文标题
Mertens展开的网络（MU-NET）：通过挡风玻璃驾驶员识别的高动态范围融合神经网络
The Mertens Unrolled Network (MU-Net): A High Dynamic Range Fusion Neural Network for Through the Windshield Driver Recognition
论文作者
论文摘要
在不受约束的环境中，通过挡风玻璃对车辆乘员的面部识别构成了许多独特的挑战，包括眩光，照明不良，驾驶员姿势和运动模糊。在本文中，我们进一步开发了自定义车辆成像系统的硬件和软件组件，以更好地克服这些挑战。在执行高动态范围（HDR）成像的物理原型系统中构建后，我们收集了一个透明壳图像捕获的小数据集。然后，我们将经典的Mertens-Kautz-Van Reeth HDR HDR融合算法作为原始化的神经网络，我们将其命名为Mertens展开的网络（MU-NET），目的是通过微调通过Windshield图像的HDR输出进行微调。然后，评估了这种新型HDR方法的重建面孔，并与预先训练的最先进（SOTA）面部识别管道中的其他传统和实验性HDR方法进行比较，从而验证了我们方法的疗效。
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.
