Description

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. 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 results shows that SWLDA better than conventional techniques in terms of robustness in suitable feature selection and classification. In this system, the system propose the use of Stepwise Linear Discriminant Analysis (SWLDA) coupled with Hidden Conditional Random Fields (HCRF) for a sequence-based FER system named SH-FER. The most prominent features were selected by proposing a robust technique called stepwise linear discriminant analysis (SWLDA that focuses on selecting the localized features from the activity frames and discriminating their class based on regression values. The purpose of using SWLDA as a feature extraction technique is to extract the localized features from faces that the previous feature extraction techniques were limited in analyzing.