Semi-supervised Contrastive Regression for Estimation of Eye Gaze

TL;DR

This paper introduces a semi-supervised contrastive regression framework that improves gaze estimation accuracy using limited labeled data, enhancing generalization for unseen face images in human-machine interfaces.

Contribution

It proposes a novel contrastive loss paradigm and a semi-supervised learning framework specifically designed for gaze estimation with small labeled datasets.

Findings

Outperforms existing contrastive learning methods for gaze estimation

Achieves good generalization on unseen face images

Reduces redundancy in embedding representations

Abstract

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.