| Presentation | 2003/12/11 Facial expression analysis by Kernel Eigenspace Method based on Class features (KEMC) using non-linear basis for separation of expression-classes Yohei KOSAKA, Kazunori KOTANI, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In the facial expression recognition by analyzing feature-vectors with linear transformation, the accuracy of recognition is depending on expression-classes. The accuracy falls sharply when the feature vector of the expression-class has a distribution with difficult linear separation in the feature-space. This paper describes a new method of facial expression analysis and recognition by using non-linear transformation for separating each expression-classes. Our new method, namely KEMC, consists of the non-linear transformation defined by kernel functions for transforming higher dimensional space and EMC (Eigenspace Method based on Class features). This paper also shows experimental results of facial expression classification bv KEMC. |
| Keyword(in Japanese) | (See Japanese page) |
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| Paper # | CS2003-127,IE2003-117 |
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| Committee | IE |
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| Conference Date | 2003/12/11(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Image Engineering (IE) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Facial expression analysis by Kernel Eigenspace Method based on Class features (KEMC) using non-linear basis for separation of expression-classes |
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| 1st Author's Name | Yohei KOSAKA |
| 1st Author's Affiliation | School of Information Science, Japan Advanced Institute of Science and Technology() |
| 2nd Author's Name | Kazunori KOTANI |
| 2nd Author's Affiliation | School of Information Science, Japan Advanced Institute of Science and Technology |
| Date | 2003/12/11 |
| Paper # | CS2003-127,IE2003-117 |
| Volume (vol) | vol.103 |
| Number (no) | 513 |
| Page | pp.pp.- |
| #Pages | 6 |
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