Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14029
Title: A novel null space-based kernel discriminant analysis for face recognition
Authors: Zhao, T
Liang, Z
Zhang, D 
Liu, Y
Issue Date: 2007
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2007, v. 4642 LNCS, p. 547-556
Abstract: The symmetrical decomposition is a powerful method to extract features for image recognition. It reveals the significant discriminative information from the mirror image of symmetrical objects. In this paper, a novel null space kernel discriminant method based on the symmetrical method with a weighted fusion strategy is proposed for face recognition. It can effectively enhance the recognition performance and shares the advantages of Null-space, kernel and symmetrical methods. The experiment results on ORL database and FERET database demonstrate that the proposed method is effective and outperforms some existing subspace methods.
Keywords: Face recognition
Symmetrical decomposition
Symmetrical null-space based kernel LDA
Weighted fusion strategy
Publisher: Springer
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
ISBN: 9783540745488
ISSN: 0302-9743
EISSN: 1611-3349
Description: 2007 International Conference on Advances in Biometrics, ICB 2007, Seoul, 27-29 August 2007
Appears in Collections:Conference Paper

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