A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor


Han Foon, Neo and Chuan Chin, Teo and Andrew Beng Jin, Teoh (2007) A Study on Optimal Face Ratio for Recognition Using Part-based Feature Extractor. In: IEEE International Conference on Signal Image Technology and Internet Based Systems, 16-19 DEC 2007, Shanghai, PEOPLES R CHINA .

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 29 Sep 2011 07:07
Last Modified: 29 Sep 2011 07:07
URII: http://shdl.mmu.edu.my/id/eprint/2949


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