A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor
This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25%, 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational po...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2007
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| Subjects: | |
| Online Access: | http://shdl.mmu.edu.my/2949/ http://shdl.mmu.edu.my/2949/1/A%20Study%20on%20Optimal%20Face%20Ratio.pdf |
| Summary: | This paper aims to investigate the optimal face ratio for recognition. Face data are normalized to several ratios, which are 25%, 50% (equivalent to right and left face), and 75% of the full-face. The advantages of using different face ratios are these face data reduce the amount of computational power and storage requirements significantly. For fair comparison, various part-based linear subspace feature extractors, namely Non-negative matrix factorization (NMF), Local NMF (LNMF) and Spatially Confined NMF (SFNMF) are used to estimate the optimal face ratio. Our results show that 75% faces are good enough to produce demonstrably recognition accuracy. |
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