Abstract
Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos theta/ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos theta/ 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.
| Original language | English |
|---|---|
| Title of host publication | ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 650-654 |
| Number of pages | 5 |
| Volume | 2 |
| ISBN (Print) | 9810475241, 9789810475246 |
| DOIs | |
| Publication status | Published - 2002 |
| Externally published | Yes |
| Event | 9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore Duration: 2002 Nov 18 → 2002 Nov 22 |
Other
| Other | 9th International Conference on Neural Information Processing, ICONIP 2002 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 02/11/18 → 02/11/22 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
True smile recognition system using neural networks. / Nakano, M.; Mitsukura, Yasue; Fukumi, M.; Akamatsu, N.
ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age. Vol. 2 Institute of Electrical and Electronics Engineers Inc., 2002. p. 650-654 1198138.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - True smile recognition system using neural networks
AU - Nakano, M.
AU - Mitsukura, Yasue
AU - Fukumi, M.
AU - Akamatsu, N.
PY - 2002
Y1 - 2002
N2 - Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos theta/ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos theta/ 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.
AB - Recently, research about man-machine interfaces has increased. Therefore application to facial expressions is expected from the development of the man-machine interface. An eigen-face method is popular in these research fields by using the principal component analysis (PCA). But in PCA, it is not easy to compute eigenvectors with a large matrix when considering the cost of calculation to adapt for time-varying processing. In order for PCA to become high-speed, the simple principal component analysis (SPCA) is applied to compress the dimensionality of portions that constitute a face. A value of cos theta/ is calculated using the eigenvector and the gray-scale image vector of each picture pattern. By using neural networks (NN), the value of cos theta/ 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.
UR - http://www.scopus.com/inward/record.url?scp=84964412322&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964412322&partnerID=8YFLogxK
U2 - 10.1109/ICONIP.2002.1198138
DO - 10.1109/ICONIP.2002.1198138
M3 - Conference contribution
AN - SCOPUS:84964412322
SN - 9810475241
SN - 9789810475246
VL - 2
SP - 650
EP - 654
BT - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PB - Institute of Electrical and Electronics Engineers Inc.
ER -