Abstract
In this paper, a novel Fisher criterion is introduced and shown to be equivalent to the traditional Fisher criterion. Based on this new Fisher criterion and simultaneous diagonalization technique, a St-subspace Fisher discriminant (St-SFD) method is developed to deal with the small sample size (S3) problem in face recognition. The proposed method overcomes some drawbacks of existing LDA based algorithms. Also, our method has good computational complexity. Two public available databases, namely ORL and FERET databases, are exploited to evaluate the proposed algorithm. Comparing with existing LDA-based methods in solving the S3 problem, the proposed S t-SFD method gives the best performance.
Original language | English |
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Title of host publication | Computational Intelligence and Security - International Conference, CIS 2005, Proceedings |
Publisher | Springer Verlag |
Pages | 933-940 |
Number of pages | 8 |
ISBN (Print) | 3540308180, 9783540308188 |
DOIs | |
Publication status | Published - 2005 |
Event | 2005 International Conference on Computational Intelligence and Security, CIS 2005 - Xi'an, China Duration: 15 Dec 2005 → 19 Dec 2005 https://link.springer.com/book/10.1007/11596448 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 3801 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 2005 International Conference on Computational Intelligence and Security, CIS 2005 |
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Country/Territory | China |
City | Xi'an |
Period | 15/12/05 → 19/12/05 |
Internet address |
Scopus Subject Areas
- Theoretical Computer Science
- General Computer Science