Face is the most dynamic part of the human body which comprises information about the feelings of people with facial expressions. In this paper, we propose a novel feature selection procedure applied to 3-Dimensional (3D) geometrical facial feature points selected from MPEG-4 Facial Definition Parameters (FDPs) in order to achieve robust classification performance. Distances between 3D feature point pairs are used to describe a facial expression. Support Vector Machine (SVM) is employed as the classifier. The system is tested on 3D facial expression database BU-3DFE and shows significant improvements with the proposed feature selection algorithm. © 2013 Springer International Publishing.
CITATION STYLE
Yurtkan, K., Soyel, H., & Demirel, H. (2014). Feature selection for enhanced 3D facial expression recognition based on varying feature point distances. In Lecture Notes in Electrical Engineering (Vol. 264 LNEE, pp. 209–217). Springer Verlag. https://doi.org/10.1007/978-3-319-01604-7_21
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