Deep learning methods have proven useful for head pose estimation, but the effect of their depth, type and input resolution based on infrared (IR) images still need to be explored. In this paper, we present a study on in-car head pose estimation on the IR images of the AutoPOSE dataset, where we extract 64 x 64 and 128 x 128 pixel cropped head images. We propose the novel networks Head Orientation Network (HON) and ResNetHG and compare them with state-of-the-art methods like the HPN model from DriveAHead on different input resolutions. In addition, we evaluate multiple depths within our HON and ResNetHG networks and their effect on the accuracy. Our experiments show that higher resolution images lead to lower estimation errors. Furthermore, we show that deep learning methods with fewer layers perform better on head orientation regression based on IR images. Our HON and ResNetHG18 architectures outperform the state-of-the-art on IR images on four different metrics, where we achieve a reduction of the residual error of up to 74%.