Presentation | 2004/3/11 Facial Expression Recognition Using Fisher Weight Maps Yusuke SHINOHARA, Nobuyuki OTSU, |
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Abstract(in English) | In recent years, much research has been done on face image analysis. There are two major approaches: local-feature-based and image-vector-based. We propose a hybrid of these two approaches. First, local features are computed at each pixel hi an image. Secondly, these features are integrated with a weight map to obtain a feature vector. The optimal weight map, called a Fisher weight map, is found by maximizing the Fisher discriminant criterion of feature vectors. Finally, the face image is classified based on the feature vector. Our experiments on facial expression recognition demonstrate the effectiveness of Fisher weight maps for objectively quantifying the importance of each facial area for classification of expressions. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Facial Expression Recognition / Discriminant Criterion / Fisher Weight Maps / Higher-order Local Auto-Correlation Features |
Paper # | PRMU2003-269 |
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Committee | PRMU |
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Conference Date | 2004/3/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Pattern Recognition and Media Understanding (PRMU) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Facial Expression Recognition Using Fisher Weight Maps |
Sub Title (in English) | |
Keyword(1) | Facial Expression Recognition |
Keyword(2) | Discriminant Criterion |
Keyword(3) | Fisher Weight Maps |
Keyword(4) | Higher-order Local Auto-Correlation Features |
1st Author's Name | Yusuke SHINOHARA |
1st Author's Affiliation | Graduate School of Information Science and Technology, University of Tokyo() |
2nd Author's Name | Nobuyuki OTSU |
2nd Author's Affiliation | Graduate School of Information Science and Technology, University of Tokyo:National Institute of Advanced Industrial Science and Technology (AIST) |
Date | 2004/3/11 |
Paper # | PRMU2003-269 |
Volume (vol) | vol.103 |
Number (no) | 737 |
Page | pp.pp.- |
#Pages | 6 |
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