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A deep learning approach to appearance-based gaze estimation under head pose variations
Conference paper

A deep learning approach to appearance-based gaze estimation under head pose variations

Hsin-Pei Sun, Cheng-Hsun Yang and Shang-Hong Lai
Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017, pp.941-946
12/2018

Abstract

Convolutional neural network Deep learning Gaze estimation Artificial Intelligence Computer Vision and Pattern Recognition Signal Processing
In this paper, we propose a deep learning based gaze estimation algorithm that estimates the gaze direction from a single face image. The proposed gaze estimation algorithm is based on using multiple convolutional neural networks (CNN) to learn the regression networks for gaze estimation from the eye images. The proposed algorithm can provide accurate gaze estimation for users with different head poses, since it explicitly includes the head pose information into the proposed gaze estimation framework. The proposed algorithm can be widely used for appearance-based gaze estimation in practice. Our experimental results show that the proposed gaze estimation system improves the accuracy of appearance-based gaze estimation under head pose variations compared to the previous methods.

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