Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICME.2019.00151 | 2019 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | DocType | ISSN |
Gaze estimation, Itracker, RNN, LSTM | Conference | 1945-7871 |
ISBN | Citations | PageRank |
978-1-5386-9553-1 | 0 | 0.34 |
References | Authors | |
5 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolong Zhou | 1 | 103 | 19.67 |
| Jianing Lin | 2 | 0 | 0.34 |
| Jiaqi Jiang | 3 | 0 | 1.01 |
| Sheng-Yong Chen | 4 | 1077 | 114.06 |