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Hybrid patch based diagonal pattern geometric appearance model for facial expression recognition
Jain Deepak Kumar1,2,3; Zhang Zhang1,2,3; Huang Kaiqi1,2,3
2016
会议名称Intelligent Visual Surveillance
会议日期2016-10-19
会议地点Beijing
摘要
Automatic Facial Expression Recognition (FER) is an imperative process in next generation Human-Machine Interaction (HMI) for clinical applications. The detailed information analysis and maximization of labeled database are the major concerns in FER approaches. This paper proposes a novel Patch-Based Diagonal Pattern (PBDP) method on Geometric Appearance Models (GAM) that extracts the features in a multi-direction for detailed information analysis. Besides, this paper adopts the co-training to learn the complementary information from RGB-D images. Finally, the Relevance Vector Machine (RVM) classifier is used to recognize the facial expression. In experiments, we validate the proposed methods on two RGB-D facial expression datasets, i.e.,EURECOMM dataset and biographer dataset. Compared to other methods, the comparative analysis regarding the recognition and error rate prove the effectiveness of the proposed PBDP-GAM in FER applications.
关键词Facial Expressions Feature Points
DOI10.1007/978-981-10-3476-3 13
引用统计
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/21194
专题智能感知与计算研究中心
通讯作者Jain Deepak Kumar
作者单位1.Center for Research on Intelligent Perception and Computing, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
3.University of Chinese Academy of Sciences, Beijing, China
推荐引用方式
GB/T 7714
Jain Deepak Kumar,Zhang Zhang,Huang Kaiqi. Hybrid patch based diagonal pattern geometric appearance model for facial expression recognition[C],2016.
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