Semi-supervised Contrastive Regression for Estimation of Eye Gaze
Somsukla Maiti Akshansh Gupta
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.