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Semi-supervised discriminant analysis method for face recognition
Wen-Sheng Chen1; Xiuli Dai1; Binbin Pan1; Yuan Yan Tang2
2015-11-01
Source PublicationInternational Journal of Wavelets, Multiresolution and Information Processing
ISSN0219-6913
Volume13Issue:6
Abstract

In face recognition (FR), a lot of algorithms just utilize one single type of facial features namely global feature or local feature, and cannot obtain better performance under the complicated variations of the facial images. To extract robust facial features, this paper proposes a novel Semi-Supervised Discriminant Analysis (SSDA) criterion via nonlinearly combining the global feature and local feature. To further enhance the discriminant power of SSDA features, the geometric distribution weight information of the training data is also incorporated into the proposed criterion. We use SSDA criterion to design an iterative algorithm which can determine the combination parameters and the optimal projection matrix automatically. Moreover, the combination parameters are guaranteed to fall into the interval [0, 1]. The proposed SSDA method is evaluated on the ORL, FERET and CMU PIE face databases. The experimental results demonstrate that our method achieves superior performance.

KeywordFace Recognition Global Feature Local Feature Semi-supervised Learning
DOIhttps://doi.org/10.1142/S0219691315500496
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Mathematics
WOS SubjectComputer Science, Software Engineering ; Mathematics, Interdisciplinary Applications
WOS IDWOS:000367521500008
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD, 5 TOH TUCK LINK, SINGAPORE 596224, SINGAPORE
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Cited Times [WOS]:9   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorWen-Sheng Chen; Binbin Pan; Yuan Yan Tang
Affiliation1.College of Mathematics and Statistics, Shenzhen Key Laboratory of Media Security, Research Center of Intelligent Analysis and Processing for HD Video, Shenzhen University, Shenzhen 518060, P. R. China
2.Department of Computer and Information Science University of Macau, Macau, P. R. China
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wen-Sheng Chen,Xiuli Dai,Binbin Pan,et al. Semi-supervised discriminant analysis method for face recognition[J]. International Journal of Wavelets, Multiresolution and Information Processing,2015,13(6).
APA Wen-Sheng Chen,Xiuli Dai,Binbin Pan,&Yuan Yan Tang.(2015).Semi-supervised discriminant analysis method for face recognition.International Journal of Wavelets, Multiresolution and Information Processing,13(6).
MLA Wen-Sheng Chen,et al."Semi-supervised discriminant analysis method for face recognition".International Journal of Wavelets, Multiresolution and Information Processing 13.6(2015).
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