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.
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Feng, X., Lv, B., Li, Z., Zhang, J. (2006). Automatic Facial Expression Recognition with AAM-Based Feature Extraction and SVM Classifier. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_69
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DOI: https://doi.org/10.1007/11925231_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
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