Release Time:2019-11-04  Hits:

Indexed by: Conference Paper

Date of Publication: 2017-06-18

Volume: 2018-February

Page Number: 1184-1188

Abstract: A novel method for appearance-based gaze estimation from massive synthetic eye images is proposed in this paper. This method is a combination of neighbor selection and gaze local regression for gaze mapping. First, a simple cascaded method using multiple k-NN(k-Nearest Neighbor) classifier is employed to select neighbors in feature space joint head pose, pupil center and eye appearance. Second, PLSR (Partial Least Square Regression) is applied to seek for a direct correlation between image feature and gaze angle. Experimental results demonstrate that the proposed method achieves state-of-The-Art accuracy below 1 degree for with-in subject gaze estimation on public synthesis eye image dataset. © 2017 IEEE.