RAZE: Region Guided Self-Supervised Gaze Representation Learning
This work addresses the need for large-scale annotated data in vision-based assistive technologies for applications such as augmented reality and human-computer interaction, offering an incremental improvement through a novel self-supervised method.
The paper tackles the problem of eye gaze estimation by proposing RAZE, a self-supervised framework that learns gaze representations from non-annotated facial images using pseudo-gaze zone classification, achieving competitive performance on benchmark datasets like CAVE, TabletGaze, MPII, and RT-GENE.
Automatic eye gaze estimation is an important problem in vision based assistive technology with use cases in different emerging topics such as augmented reality, virtual reality and human-computer interaction. Over the past few years, there has been an increasing interest in unsupervised and self-supervised learning paradigms as it overcomes the requirement of large scale annotated data. In this paper, we propose RAZE, a Region guided self-supervised gAZE representation learning framework which leverage from non-annotated facial image data. RAZE learns gaze representation via auxiliary supervision i.e. pseudo-gaze zone classification where the objective is to classify visual field into different gaze zones (i.e. left, right and center) by leveraging the relative position of pupil-centers. Thus, we automatically annotate pseudo gaze zone labels of 154K web-crawled images and learn feature representations via `Ize-Net' framework. `Ize-Net' is a capsule layer based CNN architecture which can efficiently capture rich eye representation. The discriminative behaviour of the feature representation is evaluated on four benchmark datasets: CAVE, TabletGaze, MPII and RT-GENE. Additionally, we evaluate the generalizability of the proposed network on two other downstream task (i.e. driver gaze estimation and visual attention estimation) which demonstrate the effectiveness of the learnt eye gaze representation.