Semi-supervised Contrastive Regression for Estimation of Eye Gaze
This work addresses the need for non-intrusive human-machine interfaces by improving gaze estimation, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of gaze estimation by developing a semi-supervised contrastive learning framework that uses a small labeled dataset to generalize to unseen face images, showing good performance compared to state-of-the-art techniques.
With the escalated demand of human-machine interfaces for intelligent systems, development of gaze controlled system have become a necessity. Gaze, being the non-intrusive form of human interaction, is one of the best suited approach. Appearance based deep learning models are the most widely used for gaze estimation. But the performance of these models is entirely influenced by the size of labeled gaze dataset and in effect affects generalization in performance. This paper aims to develop a semi-supervised contrastive learning framework for estimation of gaze direction. With a small labeled gaze dataset, the framework is able to find a generalized solution even for unseen face images. In this paper, we have proposed a new contrastive loss paradigm that maximizes the similarity agreement between similar images and at the same time reduces the redundancy in embedding representations. Our contrastive regression framework shows good performance in comparison to several state of the art contrastive learning techniques used for gaze estimation.