3D Gaze Estimation on Near Infrared Images Using Vision Transformers

Abstract: Gaze estimation is the process of determining where a person is looking, which has recently become a popular research area due to its broad range of applications. For example, tools that estimate gaze are used for research, medical diagnosis, virtual and augmented reality, driver assistance system, and many more. Therefore, better products are sought by many. Gaze estimation methods typically use images of only the eyes or the whole face to estimate the gaze since these methods are the most practical and convenient options. Recently, Convolutional Neural Networks (CNNs) have been appealing candidates for estimating the gaze. Nevertheless, the recent success of Vision Transformers (ViTs) in image classification tasks has introduced a new potential alternative. Hence, this work investigates the potential of using ViTs to estimate the gaze on Near-Infrared (NIR) images. This is done in terms of average error and computational complexity. Furthermore, this work examines not only pure ViTs but other models, such as hybrid ViTs and CNN-Formers, which combine CNNs and ViTs. The empirical results showed that hybrid ViTs are the only models that can outperform state-of-the-art CNNs such as MobileNetV2 and ResNet-18 while maintaining similar computational complexity to ResNet-18. The results on hybrid ViTs indicate that the convolutional stem is the most crucial part of them. Improved convolutional stems lead to better outcomes. Moreover, in this work, we defined a new training algorithm for hybrid ViTs, the hybrid Data-Efficient Image Transformer (DeiT) procedure, which has shown remarkable results. It is 3.5% better than the pretrained ResNet-18 while having the same time complexity.