A Joint Cascaded Framework for Simultaneous Eye State, Eye Center, and Gaze Estimation
Jie Zhu, Mengtang Li, Yuezhao Yu, Chao Gou · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Eye tracking technology is widely used in a range of potential interactive applications, containing biometrics recognition, emotion recognition, and virtual reality. Eye tracking includes various tasks, but most existing methods cannot accomplish multiple tasks simultaneously. The related works conduct eye localization first, followed by performing gaze estimation or eye state prediction sequentially. In this paper, we propose a unified method based on cascade regression framework to achieve multi-task of eye detection, eye state prediction, and gaze estimation simultaneously. We hypothesize that there is a correspondence between each task, hence introducing a cascade regression framework to capture the implicit relation. At each iteration, we extract appearance features and shape features from eye region to estimate eye state and gaze direction. Based on previous eye state information and gaze vectors, we further use the cascade regression to map these information to update eye location. The proposed method accomplishes three tasks, namely eye state prediction, gaze estimation, and eye localization, through learning the cascade regression. Experimental results on the benchmarks of BioID, Gi4E, MPIIGaze and UT-Multiview demonstrate that the proposed approach achieves superior performance in the aforementioned three tasks.