Facial Expression Recognition Using Contourlets and Regularized Discriminant Analysis-based Boosting Algorithm

Abstract

This paper presents a facial expression recognition based on contourlet features and a regularized discriminant analysis (RDA)-based boosting algorithm. The proposed method utilizes a RDA-based boosting algorithm with effective contourlet features to recognize the facial expressions. Entropy criterion is applied to select the informative contourlet feature which is a subset of informative and nonredundant contourlet features. RDA-based boosting algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.