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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hosseini, I. Shams, N. Amini, P. Sadri, M.S. Rahmaty, M. Rahmaty, S. |
| Copyright Year | 2006 |
| Description | Author affiliation: ECE Dept., Isfahan Univ. of Technol. (Hosseini, I.; Shams, N.; Amini, P.; Sadri, M.S.; Rahmaty, M.; Rahmaty, S.) |
| Abstract | There are different solutions to facial expression recognition problem. In this paper a novel method for recognizing six basic facial expressions plus neutral state is developed. The solution is based on principal component analysis of two basic parts of the face: eyes and mouth. The recognition rate of the proposed method highly depends on how accurate basic facial components are extracted. A new method based on wavelet-based salient points is used for this aim resulting in a high facial expression recognition rate as well as low computational complexity. An emotion subspace is defined for each kind of facial components that facial components are projected onto. Finally, recognition would be done using a soft recognition algorithm. Simulation results cover two different cases. In the first case, the same set of individuals is used for both training and testing. However, some new individuals are added to the test set in the second situation. It is shown that this system achieves better results compared to most of similar techniques |
| Starting Page | 1992 |
| Ending Page | 1995 |
| File Size | 4910665 |
| Page Count | 4 |
| File Format | |
| ISBN | 1424400384 |
| DOI | 10.1109/CCECE.2006.277815 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2006-05-07 |
| Publisher Place | Canada |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Wavelet analysis Face detection Principal component analysis Eyes Mouth Humans Image analysis Data mining Computational complexity Subspace analysis methods Facial expression Wavelet-based salient points |
| Content Type | Text |
| Resource Type | Article |
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