Comparison of Eye-gaze Detection using CNN and Vision Transformer

We propose an eye-gaze input system that utilizes a laptop PC and its inner camera. This system can discriminate the user's eye-gaze direction by using Convolutional Neural Network (CNN) or Vision Transformer (ViT). In this paper, we present the results of a comparison of the newly created eye-gaze direction discrimination model of ViT and the past model created by a CNN. We evaluated the accuracy of discrimination models created by ViT and CNN through the experiments. As a result, the ViT model has higher accuracy than the CNN model in discriminating the center direction. © 2024 The Institute of Electrical Engineers of Japan.