  Facial analysis has attracted much attention in the technology for
human-machine interface. Different methods of classification based on sparse
representation and Gabor kernels have been widely applied in the fields of
facial analysis. However, most of these methods treat face from a whole view
standpoint. In terms of the importance of different facial views, in this
paper, we present multi-view face analysis based on sparse representation and
Gabor wavelet coefficients. To evaluate the performance, we conduct face
analysis experiments including face recognition (FR) and face expression
recognition (FER) on JAFFE database. Experiments are conducted from two parts:
(1) Face images are divided into three facial parts which are forehead, eye and
mouth. (2) Face images are divided into 8 parts by the orientation of Gabor
kernels. Experimental results demonstrate that the proposed methods can
significantly boost the performance and perform better than the other methods.
