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个人信息Personal Information
教授
博士生导师
硕士生导师
性别：男
毕业院校：中科院上海硅酸盐研究所
学位：博士
所在单位：物理学院
学科：微电子学与固体电子学. 凝聚态物理
办公地点：大连理工大学科技园C座301-1办公室
联系方式：E-mail:jmbian@dlut.edu.cn.
电子邮箱：jmbian@dlut.edu.cn
Appearance-based gaze estimation using deep features and random forest regression
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论文类型：期刊论文
发表时间：2016-10-15
发表刊物：KNOWLEDGE-BASED SYSTEMS
收录刊物：SCIE、EI、Scopus
卷号：110
页面范围：293-301
ISSN号：0950-7051
关键字：Appearance; Gaze estimation; Deep features; Random forest; CNN
摘要：Conventional appearance-based gaze estimation methods employ local or global features as eye gaze appearance descriptor. But these methods don't work well under natural light with free head movement. To solve this problem, we present an appearance-based gaze estimation method using deep feature representation and feature forest regression. The deep feature is learned through hierarchical extraction of deep Convolutional Neural Network (CNN). And random forest regression with cluster-to-classify node splitting rules is used to take advantage of data distribution in sparse feature space. Experimental results demonstrate that the deep feature has a better performance than local features on calibrated gaze regression. The combination of deep features and random forest regression provides an effective solution for gaze estimation in a natural environment. (C) 2016 Elsevier B.V. All rights reserved.