True smile recognition using neural networks and simple PCA

Miyoko Nakano; Yasue Mitsukura; Minoru Fukumi; Norio Akamatsu; Fumiko Yasukata

True smile recognition using neural networks and simple PCA

Miyoko Nakano, Yasue Mitsukura, Minoru Fukumi, Norio Akamatsu, Fumiko Yasukata

Research output: Contribution to journal › Conference article › peer-review

Abstract

Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, 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 cos θ 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.

Original language	English
Pages (from-to)	631-637
Number of pages	7
Journal	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume	2773 PART 1
Publication status	Published - 2003 Dec 1
Externally published	Yes
Event	7th International Conference, KES 2003 - Oxford, United Kingdom Duration: 2003 Sept 3 → 2003 Sept 5

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Cite this

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title = "True smile recognition using neural networks and simple PCA",

abstract = "Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, 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 cos θ 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.",

author = "Miyoko Nakano and Yasue Mitsukura and Minoru Fukumi and Norio Akamatsu and Fumiko Yasukata",

year = "2003",

month = dec,

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journal = "Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)",

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AU - Nakano, Miyoko

AU - Mitsukura, Yasue

AU - Fukumi, Minoru

AU - Akamatsu, Norio

AU - Yasukata, Fumiko

PY - 2003/12/1

Y1 - 2003/12/1

N2 - Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, 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 cos θ 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.

AB - Recently, an eigenface method by using the principal component analysis (PCA) is popular in a filed of facial expressions recognition. In this study, 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 cos θ 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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M3 - Conference article

AN - SCOPUS:8344275112

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SP - 631

EP - 637

JO - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

JF - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

T2 - 7th International Conference, KES 2003

Y2 - 3 September 2003 through 5 September 2003

ER -

True smile recognition using neural networks and simple PCA

Abstract

ASJC Scopus subject areas

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