A discriminant pseudo Zernike moments in face recognition
This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher's Linear Discriminant to achieve a stable numerical computation and good generalization in...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Australian Computer Society
2006
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/1982/ http://shdl.mmu.edu.my/1982/1/A%20discriminant%20pseudo%20Zernike%20moments%20in%20face%20recognition.pdf |
| Summary: | This paper introduces a novel discriminant moment-based method as a feature extraction technique for face recognition. In this method, pseudo Zernike moments are performed before the application of Fisher's Linear Discriminant to achieve a stable numerical computation and good generalization in small-sample-size problems. Fisher's Linear Discriminant uses pseudo Zernike moments to derive an enhanced subset of moment features by maximizing the between-class scatter, while minimizing the within-class scatter, which leads to a better discrimination and classification performance. Experimental results show that the proposed method achieves superior performance with a recognition rate of 97.51% in noise free environment and 97.12% in noise induced environment for the Essex Face94 database. For the Essex Face95 database, the recognition rates obtained are 91.73% and 90.30% in noise free and noise induced environments, respectively. |
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