Robust Multi-Camera View Face Recognition

Abstract

This paper presents multi-appearance fusion of principal component analysis (PCA) and generalization of linear discriminant analysis (LDA) for multi-camera view offline face recognition (verification) system. The generalization of LDA has been extended to establish correlations between the face classes in the transformed representation and this is called canonical covariate. The proposed system uses Gabor filter banks for characterization of facial features by spatial frequency, spatial locality and orientation to compensate to the variations of face instances occurred due to illumination, pose and facial expression changes. Convolution of Gabor filter bank to face images produces Gabor face representations with high-dimensional feature vectors. PC A 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 trained with augmented fused set of features and perform the recognition task. The system has been evaluated with UMIST face database consisting of multi-view faces. The experimental results demonstrate the efficiency and robustness of the proposed system for multi-view face images with high recognition rates. Complexity analysis of the proposed system is also presented at the end of the experimental results.