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佰斯特铁虫 (小有名气)
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[求助]
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Ei检索号 Facial Expression Recognition under Partial Occlusion Based on Weber Local Descriptor Histogram and Decision Fusion |
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Paulwolf
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佰斯特: 金币+5, ★★★★★最佳答案 2015-03-10 08:37:27
sunshan4379: LS-EPI+1, 感谢应助！ 2015-03-10 09:02:33
感谢参与，应助指数 +1
佰斯特: 金币+5, ★★★★★最佳答案 2015-03-10 08:37:27
sunshan4379: LS-EPI+1, 感谢应助！ 2015-03-10 09:02:33
 Accession number: 20144300120595 Title: Facial expression recognition under partial occlusion based on Weber Local Descriptor histogram and decision fusion Authors: Liu, Shuaishi1 Email author liu-shuaishi@126.com; Zhang, Yan1; Liu, Keping1 Author affiliation: 1 School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun, China Corresponding author: Liu, Shuaishi Source title: Chinese Control Conference, CCC Abbreviated source title: Chinese Control Conf., CCC Issue date: September 11, 2014 Publication year: 2014 Pages: 4664-4668 Article number: 6895725 Language: English ISSN: 19341768 E-ISSN: 21612927 ISBN-13: 9789881563842 Document type: Conference article (CA) Conference name: Proceedings of the 33rd Chinese Control Conference, CCC 2014 Conference date: July 28, 2014 - July 30, 2014 Conference location: Nanjing, China Conference code: 108070 Sponsor: Systems Engineering Society of China; Technical Committee on Control Theory of Chinese Association of Automation Publisher: IEEE Computer Society Abstract: In order to solve the problem which the key information of facial expression missed under partial occlusion, this paper proposes a facial expression recognition method based on Weber Local Descriptor (WLD) histogram feature and decision fusion. Firstly, the image is divided into several non-overlapping rectangle regions with equal size and the WLD is used to extract the features of each region. The purpose of that is to extract face image spatial information and to be ready for the subsequent decision fusion. Secondly, each region is further divided into blocks and the histogram of each sub-block is computed and combined as the region features so as to extract the discriminative features accurately as many as possible from each region. Finally, in order to reduce the influence of partial occlusion for facial expression, we design a classifier for the histogram features of each region and the outputs of all classifiers are fused by decision rule to determine the class of test facial expression image. The proposed method achieves better performance in JAFFE database with eyes occlusion and mouth occlusion. Experimental results show that the method is robust to facial expression recognition under partial occlusion. © 2014 TCCT, CAA. Number of references: 16 Main heading: Face recognition Controlled terms: Gesture recognition - Graphic methods - Human computer interaction - Image processing Uncontrolled terms: Decision fusion - Discriminative features - Expression recognition - Facial expression recognition - Histogram features - Partial occlusions - Spatial informations - WLD histogram Classification code: 461.4 Ergonomics and Human Factors Engineering - 716 Telecommunication; Radar, Radio and Television - 741 Light, Optics and Optical Devices - 902.1 Engineering Graphics DOI: 10.1109/ChiCC.2014.6895725 Database: Compendex Compilation and indexing terms, © 2015 Elsevier Inc. |
2楼2015-03-09 22:44:52
