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

2 引用 (Scopus)

抜粋

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.

元の言語English
ページ(範囲)631-637
ページ数7
ジャーナルLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
2773 PART 1
出版物ステータスPublished - 2003 12 1
外部発表Yes
イベント7th International Conference, KES 2003 - Oxford, United Kingdom
継続期間: 2003 9 32003 9 5

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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