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In this paper, we deal with a face recognition method for the emotional face images. Since the face recognition is one of the most natural and straightforward biometric methods, there have been various research works. However, most of them are focused on the expressionless face images and have had a very difficult problem if we consider the facial expression. In real situations, however, it is required to consider the emotional face images. Here, three basic human emotions such as happiness, sadness, and anger are investigated for the face recognition. And, this situation requires a robust face recognition algorithm then we use a fuzzy Fisher’s Linear Discriminant (FLD) algorithm with the wavelet transform. The fuzzy Fisherface is a statistical method that maximizes the ratio of between-scatter matrix and within-scatter matrix and also handles the fuzzy class information. The experimental results obtained for the CBNU face databases reveal that the approach presented in this paper yields better recognition performance in comparison with the results obtained by other recognition methods.
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?? ?? ?? ??The face recognition based on the independent component analysis and Fisher transform of Gabor features--《Microcomputer Information》2007年22期
The face recognition based on the independent component analysis and Fisher transform of Gabor features
YANG CHUNHUI QIU TIANSHUANG
(School of electronic and Information Engineering,DLUT,Dalian 116024,China)
This paper proposes an improved facial recognition algorithm based on independent component analysis and Fisher transform of Gabor features. In order to reduce the error rate of face recognition,the algorithm represents the local detail of face image feature through Gabor transform,extracts the features respectively based on different points of view,and classifies throu-gh SVM. Experiment witnesses positive effect of improvement algorithm of face recognition.
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【References】
Chinese Journal Full-text Database
LIU Yong-jun1,SONG Dong-xing1,HE Shi-ming1,CHEN Cai-kou2 (1.Department of Software Engineering,Changshu Insitute of Technology,Changshu 215500,China 2.College of Information Engineering,Yangzhou University,Yangzhou 225009,China);[J];Journal of Changshu Institute of T2008-02
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GAO Qinghua,LI Jianhua,HOU jiandang (School of Electronic and Information Engineering,Dalian University of Technology,Dalian 116023);[A];[C];2007
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2.School of Automation Engineering,UEST of China 610054;[A];[C];2004
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YIN Xing-Yun~1,XIE Ling~2(1.Department of Computer,HuainanTeacher College,Huainan .Anhui Baihu Valve Factory,Hefei 230000,China);[J];Journal of Anhui Technical Teachers C2005-05
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Yin Xingyun1,2 Wang Xun1 Dong Lanfang1 Wan Shouhong1 (1. Department of Computer Science & Technology, University of & Technology of China Hefei 230027;
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(C)2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reservedIntegrating iconic and structured matching | SpringerLink
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ECCV 1998:
Integrating iconic and structured matchingR. B. FisherA. MacKirdyConference paperSeveral investigations [11,16,19–21] have recently been undertaken into object recognition based on matching image intensity neighborhoods rather than geometric matching of features extracted from the images. These projects have used small subwindows or complete image regions and matching has been based on the similarity of extracted descriptors to previously stored descriptors. One characteristic common to these approaches is the representation of objects as a whole, rather than as a structured ensemble. This paper describes an extension to these approaches wherein a set of related features recognized at an earlier iteration also contribute to the complete object recognition. The paper describes an iconic, or image-based, matching approach that incorporates an element of geometric matching and shows that use of the subfeatures improves matching efficiency, position accuracy and completeness.This is a preview of subscription content,
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