Facial expression recognition based on feature selection by quadratic mutual information / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 510-514, 2008.
Article
in Chinese
| WPRIM
| ID: wpr-291201
ABSTRACT
To solve the problem of imprecise positioning of feature point and of the feature data redundancy in facial expression recognition by active appearance models (AAM), the automatic adjustment of initial model for AAM fitting is proposed in this paper. The specific aims are to improve the precision of positioning and to more effectively reflect the variation of expressions by acquired features. The problem of feature selection is resolved by adopting quadratic mutual information and reducing the feature dimension. The support vector machine (SVM) classifier is used for expression recognition. The experimental results on CAS-PEAL facial expression database show that the proposed method effectively improves the performance of facial expression recognition, the maximum recognition rate being 83.33%.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Computer Simulation
/
Signal Processing, Computer-Assisted
/
Pattern Recognition, Automated
/
Image Interpretation, Computer-Assisted
/
Facial Expression
/
Methods
/
Models, Biological
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2008
Type:
Article
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