An improved boosting algorithm and its application to facial emotion recognition

This paper develops a regularized discriminant analysis (RDA)-based boosting algorithm, and its application of the facial emotion recognition. The RDA-based boosting algorithm uses RDA as a learning rule in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses a particle swarm optimization algorithm to estimate optimal parameters in RDA. In this work, the proposed RDA-based boosting is used in the facial emotion recognition, and achieves a good performance. In the facial emotion recognition, contourlet features are extracted and followed by an entropy criterion to select the informative contourlet features which is a subset of informative and non-redundant contourlet features. Experiment results demonstrate that the proposed RDA-based boosting can accurately and robustly recognize facial emotions.