Multiview Gabor face recognition by fusion of PCA and canonical covariate through feature weighting

In this paper, fusion of Principal Component Analysis (PCA) and generalization of Linear Discriminant Analysis (LDA) in the context of multiview face recognition is proposed. The generalization of LDA is extended to establish correlation between face classes in the transformed representation, which is called canonical covariate. The proposed work uses Gabor filter bank for extracting facial features characterized by spatial frequency, spatial locality and orientation to compensate the variations in face that occur due to change in illumination, pose and facial expression. Convolution of Gabor filter bank with face images produces Gabor face representations with high dimensional feature vectors. PCA and canonical covariate are then applied on the Gabor face representations to reduce the high dimensional feature spaces into low dimensional Gabor eigenfaces and Gabor canonical faces. Reduced eigenface vector and canonical face vector are fused together using weighted mean fusion rule. Finally, support vector machines have been trained with augmented fused set of features to perform recognition task. The proposed system has been evaluated with UMIST face database and performs with higher recognition accuracy for multi-view face images.