PerimetryNet: A multiscale fine grained deep network for three‐dimensional eye gaze estimation using visual field analysis

Author:

Yu Shuqing1,Wang Zhihao2,Zhou Shuowen34,Yang Xiaosong5,Wu Chao6,Wang Zhao1ORCID

Affiliation:

1. College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

2. School of Software Technology Zhejiang University Hangzhou China

3. Department of Ophthalmology Sir Run Run Shaw Hospital Hangzhou China

4. School of Medicine Zhejiang University Hangzhou China

5. National Centre for Computer Animation Bournemouth University Bournemouth UK

6. School of Public Affairs Zhejiang University Hangzhou China

Abstract

AbstractThree‐dimensional gaze estimation aims to reveal where a person is looking, which plays an important role in identifying users' point‐of‐interest in terms of the direction, attention and interactions. Appearance‐based gaze estimation methods could provide relatively unconstrained gaze tracking from commodity hardware. Inspired by medical perimetry test, we have proposed a multiscale framework with visual field analysis branch to improve estimation accuracy. The model is based on the feature pyramids and predicts vision field to help gaze estimation. In particular, we analysis the effect of the multiscale component and the visual field branch on challenging benchmark datasets: MPIIGaze and EYEDIAP. Based on these studies, our proposed PerimetryNet significantly outperforms state‐of‐the‐art methods. In addition, the multiscale mechanism and visual field branch can be easily applied to existing network architecture for gaze estimation. Related code would be available at public repository https://github.com/gazeEs/PerimetryNet.

Funder

Arts and Humanities Research Council

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation of Ningbo Municipality

Publisher

Wiley

Subject

Computer Graphics and Computer-Aided Design,Software

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