Abstract
In this paper, an effective method is proposed for automatic facial expression recognition from static images. First, a modified Active Appearance Model (AAM) is used to locate facial feature points automatically. Then, based on this, facial feature vector is formed. Finally, SVM classifier with a sample selection method is adopted for expression classification. Experimental results on the JAFFE database demonstrate an average recognition rate of 69.9% for novel expressers, showing that the proposed method is promising. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Feng, X., Lv, B., Li, Z., & Zhang, J. (2006). Automatic facial expression recognition with AAM-based feature extraction and SVM classifier. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4293 LNAI, pp. 726–733). Springer Verlag. https://doi.org/10.1007/11925231_69
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