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论文类型：期刊论文

发表时间：2018-01-01

发表刊物：KNOWLEDGE-BASED SYSTEMS

收录刊物：SCIE、EI

卷号：139

页面范围：41-49

ISSN号：0950-7051

关键字：Gaze estimation; Neighbor selection; Learning-by-synthesis; Cross-subject

摘要：Appearance-based gaze estimation works well in inferring human gaze under real-world condition. But one of the significant limitations in appearance-based methods is the need for huge amounts of training data. Eye image synthesis addresses this problem by generating huge amounts of synthetic eye images with computer graphics. To fully use the large-scale synthetic eye images, a simple-but-effective appearance-based gaze estimation framework with neighbor selection is proposed in this paper. The proposed framework hierarchically fuses multiple k-NN queries (in head pose, pupil center and eye appearance spaces) to choose closest samples with more relevant features. Considering the structure characters of the closet samples, neighbor regression methods then can be applied to predict the gaze directions. Experimental results demonstrate that the representative neighbor regression methods under the proposed framework achieve better performance for within-subject and cross-subject gaze estimation. (C) 2017 Elsevier B.V. All rights reserved.