Learning A 3D Gaze Estimator with Improved Itracker Combined with Bidirectional LSTM
Xiaolong Zhou, Jianing Lin, Jiaqi Jiang, Shengyong Chen · 2019
Free-head 3D gaze estimation which outputs gaze vector in 3D space has wide application in human-computer interaction. In this paper, we propose a novel 3D gaze estimator by improving the Itracker and employing a many-to-one bidirectional LSTM (bi-LSTM). First, we improve the conventional Itracker by removing the face-grid and reducing one network branch via concatenating the two-eye region images to predict the subject's gaze of a single frame. Then, we employ the bi-LSTM to fit the temporal information between frames to estimate gaze vector for video sequence. Experimental results show that our improved Itracker obtains 11.6% significant improvement over the state-of-the-art methods on MPIIGaze dataset (single image frame) and has robust estimation accuracy for different image resolutions. Moreover, experimental results on EyeDiap dataset (video sequence) further bring 3% accuracy improvement by employing the bi-LSTM.