Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition: a clustering-based approach
Pattern Recognition Letters
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Evolutionary feature synthesis for facial expression recognition
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Application of NSGA-II to feature selection for facial expression recognition
Computers and Electrical Engineering
Facial Expression Recognition Using Facial Movement Features
IEEE Transactions on Affective Computing
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
MPEG-4 facial animation technology: survey, implementation, and results
IEEE Transactions on Circuits and Systems for Video Technology
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Automatic recognition of facial movements and expressions with high recognition rates is essential for human computer interaction. In this paper, we propose a feature selection procedure for improved facial expression recognition utilizing 3-Dimensional (3D) geometrical facial feature point positions. The proposed method classifies expressions in six basic emotional categories which are anger, disgust, fear, happiness, sadness and surprise. The most discriminative features are selected by the proposed method based on entropy changes during expression deformations of the face. Developed system uses Support Vector Machine (SVM) classifier organized in two levels. The system performance is evaluated on 3D facial expression database, BU-3DFE. The experimental results on classification performance are superior or comparable with the results of the recent methods available in the literature.