Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33506
Title: Regularization of LDA for face recognition : a post-processing approach
Authors: Zuo, W
Wang, K
Zhang, D 
Yang, J
Issue Date: 2005
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and Lecture notes in bioinformatics), 2005, v. 3723 LNCS, p. 377-391 How to cite?
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 
Abstract: When applied to high-dimensional classification task such as face recognition, linear discriminant analysis (LDA) can extract two kinds of discriminant vectors, those in the null space (irregular) and those in the range space (regular) of the within-class scatter matrix. Recently, regularization techniques, which alleviate the over-fitting to the training set, have been used to further improve the recognition performance of LDA. Most current regularization techniques, however, are pre-processing approaches and can't be used to regularize irregular discriminant vectors. This paper proposes a post-processing method, 2D-Gaussian filtering, for regularizing both regular and irregular discriminant vectors. This method can also be combined with other regularization techniques. We present two LDA methods, regularization of subspace LDA (RSLD) and regularization of complete Fisher discriminant framework (RCFD) and test them on the FERET face database. Post-processing is shown to improve the recognition accuracy in face recognition.
Description: 2nd International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2005, Beijing, 16 October 2005
URI: http://hdl.handle.net/10397/33506
ISBN: 3540292292
9783540292296
ISSN: 0302-9743
DOI: 10.1007/11564386_29
Appears in Collections:Conference Paper

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