Estimation of User-Specific Visual Attention Based on Gaze Information of Similar Users
Yuya Moroto, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama · 2019
This paper proposes an estimation of user-specific visual attention based on gaze information of similar users. The proposed method estimates the user-specific visual attention by using the eye gaze data of other similar users. Then the similar users are selected based on the past eye gaze data of the target user. Although introducing the eye gaze data of the similar users into the estimation of user-specific visual attention is a simple approach, it can break the limitation of estimation performance. This approach is the main contribution of this paper. Experimental results show the effectiveness.