A Novel Mechanism of Face Recognition Using Stepwise Linear Discriminant Analysis and Linear Vector Quantization Classifiers

Abstract

Face recognition is a hard and special case of computer vision and pattern recognition. It is a challenging problem due to various kinds of variations of face images. This paper proposes a robust face recognition system. Here stepwise linear discriminant analysis (SWLDA) is used for the feature extraction and Linear Vector Quantization (LVQ) Classifier is used for face recognition. The main focus of SWLDA is to select localized features from the face. In order to increase the low-between-class variance and to reduce within-class-variance among different expression classes and use F-test value through which results are analyzed. In recognition, firstly face is detected using canny edge detection method, after face detection SWLDA is employed to extract the face features, and end linear vector quantization is applied for face recognition. To achieve optimum results and increase the robustness of the proposed system, experiments are performed on various different samples of face image, which consist of face image with the different pose and facial expression in order to validate the system, we use two famous datasets which include Yale and ORL face database.