Improving Domain Generalization in Appearance-Based Gaze Estimation With Consistency Regularization

Gaze estimation, a method for understanding human behavior by analyzing where a person is looking, has significant applications in various fields including advertising, driving assistance, medical diagnostics, and human-computer interaction. Although appearance-based methods have shown promising performance in uncontrolled environments, they often perform poorly when applied to similar but different domains due to variances in image quality, gaze distribution, and illumination. To overcome this limitation, this study aims to improve the domain generalization of appearance-based gaze estimation models using deep learning techniques. We propose an end-to-end deep learning approach that facilitates domain-agnostic feature learning and introduce a novel loss function, spherical gaze distance (SGD), and a regularization method, gaze consistency regularization (GCR). Our experiments, conducted using three commonly used datasets for appearance-based gaze estimation: ETH-XGaze, MPIIGaze, and GazeCapture, demonstrate the effectiveness of SGD and GCR. The results show that the proposed approach outperforms all the state-of-the-art methods on the domain generalization task and significantly improves performance when SGD and GCR are combined. These findings have important implications for the field of gaze estimation, suggesting that the proposed method could enhance the robustness and generalizability of gaze estimation models.