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)|
|巻||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 3 → 2003 9月 5
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）