A robust feature extraction method for human facial expressions recognition systems

Feature extraction is one of the most important modules for Facial Expression Recognition (FER) systems, which deals with getting the distinguishable features each expression and quantizing it as a discrete symbol. In this paper, we have proposed the novel robust feature extraction technique for the FER systems called Stepwise Linear Discriminant Analysis (SWLDA). This technique focuses on the selection of localized features from the facial expression images and discriminate their classes on the basis of regression values i.e. partial F-test. The proposed technique is then compared with conventional techniques such as LDA in combination with ICA. The results shows that SWLDA better than conventional techniques in terms of robustness in suitable feature selection and classification.