TY - JOUR
T1 - True smile recognition using neural networks and simple PCA
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
SN - 0302-9743
VL - 2773 PART 1
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 -