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Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features

Authors Liao, Shu HKUST affiliated (currently or previously)
Fan, Wei HKUST affiliated (currently or previously)
Chung, Albert Chi Shing View this author's profile
Yeung, Dit-Yan View this author's profile
Issue Date 2006
Source Proceedings - International Conference on Image Processing, ICIP 2006, Atlanta, GA, United States, 8 - 11 October 2006, Category number06CH37815, Code 82528 , 2006, Article number 4106617, p. 665-668
Summary 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 null-space 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.
ISSN 1522-4880
ISBN 978-1-4244-0481-0
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.
Language English
Format Conference paper
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