Facial expression recognition using geometric and appearance features

Authors:
Jingying Chen;Dan Chen;Yujiao Gong;Meng Yu;Kun Zhang;Lizhe Wang
Affiliations:
Central China Normal University, Wuhan, China;China University of Geosciences Wuhan, China;Central China Normal University, Wuhan, China;University of Abertay, Dundee, UK;Central China Normal University, Wuhan, China;Chinese Academy of Sciences, Beijing, China
Venue:
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Year:
2012

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Abstract

A novel method using hybrid geometric and appearance features of the difference between the neutral and fully expressive facial expression images is proposed for facial expression recognition in this paper. The difference tends to emphasize the facial parts that are changed from the neutral to expressive face and eliminate in that way the identity of the facial image. The hybrid features include facial feature point displacements and local texture differences between the normalized neutral and expressive facial expression images. The proposed method achieved an average accuracy of 95% in the extended Cohn-Kanade database with a Support Vector Machine (SVM) classification method.