Application of NSGA-II to feature selection for facial expression recognition

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.