Global and Local Information Based Spherical Marginal Fisher Analysis for Face Recognition
| Autor: | Mingzhi Qu, Chengcheng Jia, Shuchao Pang, Erping Pang, Rui Liu, Zhezhou Yu |
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| Rok vydání: | 2013 |
| Předmět: |
business.industry
Fisher kernel Marginal fisher analysis Sample (statistics) Pattern recognition Library and Information Sciences Linear discriminant analysis Computer Graphics and Computer-Aided Design Facial recognition system Computational Theory and Mathematics Face (geometry) Content (measure theory) Statistics Artificial intelligence Kernel Fisher discriminant analysis business Information Systems Mathematics |
| Zdroj: | Journal of Information and Computational Science. 10:1025-1034 |
| ISSN: | 1548-7741 |
| Popis: | We proposed a new face recognition algorithm, termed Spherical Marginal Fisher Analysis (SMFA). Different from traditional Marginal Fisher Analysis (MFA) in which we don’t select a certain number of nearest samples between different classes, but contain all the needed samples in some content. Meanwhile, we add the information between sample centers as applied in Linear Discriminant Analysis (LDA). Experimental results on the ORL and Yale face databases show our method outperforms other linear methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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