Face recognition using lidar presents challenges arising from high dimensionality and data sparsity, especially at longer distances. This paper proposes a novel approach for face recognition via automotive lidar. The approach leverages a combination of deep learning and point cloud processing techniques. After identification of the facial point clouds, an alpha-shaped convex hull is employed for regional linearization, resulting in the creation of a depth image. This depth image is then fed to a convolutional neural network architecture, BasicNet, specifically trained for face recognition. The approach is evaluated on a dataset comprising 52 individuals acquired using two lidar sensors with different point densities. The individuals walked at distances ranging from 5 to 18 meters from the sensors. The approach achieves interesting results on this challenging dataset, thereby challenging the notion that lidar sensors are privacy-preserving.