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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
Conference NameIntelligent Visual Surveillance
Conference Date2016-10-19
Conference PlaceBeijing
Abstract
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.
KeywordFacial Expressions Feature Points
DOI10.1007/978-981-10-3476-3 13
Citation statistics
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21194
Collection智能感知与计算研究中心
Corresponding AuthorJain Deepak Kumar
Affiliation1.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
Recommended Citation
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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