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) |
| 巻 | 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 3 → 2003 9 5 |
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
フィンガープリント True smile recognition using neural networks and simple PCA' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。
これを引用
Nakano, M., Mitsukura, Y., Fukumi, M., Akamatsu, N., & Yasukata, F. (2003). True smile recognition using neural networks and simple PCA. Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), 2773 PART 1, 631-637.