Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
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
A survey of affect recognition methods: audio, visual and spontaneous expressions
Proceedings of the 9th international conference on Multimodal interfaces
Multi-objective Feature Selection with NSGA II
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Pattern recognition using discriminative feature extraction
IEEE Transactions on Signal Processing
Bilinear Models for 3-D Face and Facial Expression Recognition
IEEE Transactions on Information Forensics and Security
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Decision boundary feature extraction for neural networks
IEEE Transactions on Neural Networks
Facial expression recognition using 3D facial feature distances
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
A comparative study of wavelet families for classification of wrist motions
Computers and Electrical Engineering
Feature selection for improved 3D facial expression recognition
Pattern Recognition Letters
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Facial expression recognition generally requires that faces be described in terms of a set of measurable features. The selection and quality of the features representing each face have a considerable bearing on the success of subsequent facial expression classification. Feature selection is the process of choosing a subset of features in order to increase classifier efficiency and allow higher classification accuracy. Many current dimensionality reduction techniques, used for facial expression recognition, involve linear transformations of the original pattern vectors to new vectors of lower dimensionality. In this paper, we present a methodology for the selection of features that uses nondominated sorting genetic algorithm-II (NSGA-II), which is one of the latest genetic algorithms developed for resolving problems with multiobjective approach with high accuracy. In the proposed feature selection process, NSGA-II optimizes a vector of feature weights, which increases the discrimination, by means of class separation. The proposed methodology is evaluated using 3D facial expression database BU-3DFE. Classification results validates the effectiveness and the flexibility of the proposed approach when compared with results reported in the literature using the same experimental settings.