Presentation 2003/6/26
True Smile Recognition Using Neural Networks
Miyoko NAKANO, Yasue MITSUKURA, Minoru FUKUMI, Norio AKAMATSU, Fumiko YASUKATA,
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Abstract(in English) An eigenface method by using the principal component analysis (PCA) is popular in a field of facial recognition's research. However, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In this paper, in order to achieve high-speed PCA, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. By using Neural Networks (NN), the difference in value of cosθ, calculated using an eigenvector by SPCA and a face image, between true and false (plastic) smiles is clarified and the true smile is discriminated. Finally, in order to show the effectiveness of the proposed face classification method for true or false smile, computer simulations are done with real images.
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Keyword(in English) smiles recognition / simple principal component analysis / neural networks / recognition of facial expressions
Paper # HIP2003-21
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Committee HIP
Conference Date 2003/6/26(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) True Smile Recognition Using Neural Networks
Sub Title (in English)
Keyword(1) smiles recognition
Keyword(2) simple principal component analysis
Keyword(3) neural networks
Keyword(4) recognition of facial expressions
1st Author's Name Miyoko NAKANO
1st Author's Affiliation Faculty of Nursing, Fukuoka Prefectural University()
2nd Author's Name Yasue MITSUKURA
2nd Author's Affiliation Department of Information Science and Intelligent, University of Tokushima
3rd Author's Name Minoru FUKUMI
3rd Author's Affiliation Department of Information Science and Intelligent, University of Tokushima
4th Author's Name Norio AKAMATSU
4th Author's Affiliation Department of Information Science and Intelligent, University of Tokushima
5th Author's Name Fumiko YASUKATA
5th Author's Affiliation Faculty of Nursing, Fukuoka Prefectural University
Date 2003/6/26
Paper # HIP2003-21
Volume (vol) vol.103
Number (no) 165
Page pp.pp.-
#Pages 6
Date of Issue