Abstract
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.
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Yurtkan, K., Soyel, H., Demirel, H. (2013). Feature Selection for Enhanced 3D Facial Expression Recognition Based on Varying Feature Point Distances. In: Gelenbe, E., Lent, R. (eds) Information Sciences and Systems 2013. Lecture Notes in Electrical Engineering, vol 264. Springer, Cham. https://doi.org/10.1007/978-3-319-01604-7_21
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DOI: https://doi.org/10.1007/978-3-319-01604-7_21
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