RAZE: Region Guided Self-Supervised Gaze Representation Learning

• URL: http://arxiv.org/abs/2208.02485v2
• Date: Fri, 5 Aug 2022 13:02:04 GMT
• Title: RAZE: Region Guided Self-Supervised Gaze Representation Learning
• Authors: Neeru Dubey, Shreya Ghosh, Abhinav Dhall
• Abstract summary: RAZE is a Region guided self-supervised gAZE representation learning framework which leverage from non-annotated facial image data. Ize-Net is a capsule layer based CNN architecture which can efficiently capture rich eye representation.
• Score: 5.919214040221055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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