RGBD Based Gaze Estimation via Multi-Task CNN

  • Dongze Lian Shanghaitech University
  • Ziheng Zhang Shanghaitech University
  • Weixin Luo Shanghaitech University
  • Lina Hu Shanghaitech University
  • Minye Wu Shanghaitech University
  • Zechao Li Nanjing University of Science and Technology
  • Jingyi Yu Shanghai Tech University
  • Shenghua Gao Shanghaitech University

Abstract

This paper tackles RGBD based gaze estimation with Convolutional Neural Networks (CNNs). Specifically, we propose to decompose gaze point estimation into eyeball pose, head pose, and 3D eye position estimation. Compared with RGB image-based gaze tracking, having depth modality helps to facilitate head pose estimation and 3D eye position estimation. The captured depth image, however, usually contains noise and black holes which noticeably hamper gaze tracking. Thus we propose a CNN-based multi-task learning framework to simultaneously refine depth images and predict gaze points. We utilize a generator network for depth image generation with a Generative Neural Network (GAN), where the generator network is partially shared by both the gaze tracking network and GAN-based depth synthesizing. By optimizing the whole network simultaneously, depth image synthesis improves gaze point estimation and vice versa. Since the only existing RGBD dataset (EYEDIAP) is too small, we build a large-scale RGBD gaze tracking dataset for performance evaluation. As far as we know, it is the largest RGBD gaze dataset in terms of the number of participants. Comprehensive experiments demonstrate that our method outperforms existing methods by a large margin on both our dataset and the EYEDIAP dataset.

Published
2019-07-17
Section
AAAI Technical Track: Human-AI Collaboration