  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 make 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. 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 (SVM) have trained with augmented fused set of features
and perform the recognition task. The system has been evaluated with UMIST face
database consisting of multiview 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.
