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Title: Facial expression recognition using advanced local binary patterns, Tsallis entropies and global appearance features
Authors: Liao, Shu
Fan, Wei
Chung, Albert C. S.
Yeung, Dit-Yan
Keywords: Facial recognition
Machine version
Issue Date: Oct-2006
Citation: Proceedings 2006 IEEE Conference on Image Processing, ICIP 2006, 8-11 October, 2006, Atlanta, GA, USA, p. 665-668
Abstract: This paper proposes a novel facial expression recognition approach based on two sets of features extracted from the face images: texture features and global appearance features. The first set is obtained by using the extended local binary patterns in both intensity and gradient maps and computing the Tsallis entropy of the Gabor filtered responses. The second set of features is obtained by performing nullspace based linear discriminant analysis on the training face images. The proposed method is evaluated by extensive experiments on the JAFFE database, and compared with two widely used facial expression recognition approaches. Experimental results show that the proposed approach maintains high recognition rate in a wide range of resolution levels and outperforms the other alternative methods.
Rights: © 2006 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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