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 |
| _version_ | 1848790192908926976 |
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| author | Neo, Han Foon Teo, Chuan Chin Teoh, Andrew Beng Jin |
| author_facet | Neo, Han Foon Teo, Chuan Chin Teoh, Andrew Beng Jin |
| author_sort | Neo, Han Foon |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:08:43Z |
| format | Conference or Workshop Item |
| id | mmu-2949 |
| institution | Multimedia University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:08:43Z |
| publishDate | 2007 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | mmu-29492021-09-21T07:51:34Z http://shdl.mmu.edu.my/2949/ A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor Neo, Han Foon Teo, Chuan Chin Teoh, Andrew Beng Jin T Technology (General) QA75.5-76.95 Electronic computers. Computer science 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. IEEE 2007-12 Conference or Workshop Item NonPeerReviewed text en http://shdl.mmu.edu.my/2949/1/A%20Study%20on%20Optimal%20Face%20Ratio.pdf Neo, Han Foon and Teo, Chuan Chin and Teoh, Andrew Beng Jin (2007) A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor. In: 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System, 16-18 Dec. 2007, Shanghai, China. https://ieeexplore.ieee.org/document/4618846 10.1109/SITIS.2007.52 10.1109/SITIS.2007.52 10.1109/SITIS.2007.52 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science Neo, Han Foon Teo, Chuan Chin Teoh, Andrew Beng Jin A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title | A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title_full | A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title_fullStr | A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title_full_unstemmed | A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title_short | A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor |
| title_sort | study on optimal face ratio for recognition using part-based feature extractor |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/2949/ http://shdl.mmu.edu.my/2949/ http://shdl.mmu.edu.my/2949/ http://shdl.mmu.edu.my/2949/1/A%20Study%20on%20Optimal%20Face%20Ratio.pdf |