True smile recognition using neural networks and simple PCA

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

研究成果: Conference article査読

2 被引用数 (Scopus)


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.

ジャーナルLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2773 PART 1
出版ステータスPublished - 2003 12月 1
イベント7th International Conference, KES 2003 - Oxford, United Kingdom
継続期間: 2003 9月 32003 9月 5

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)


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