  Even though face recognition in frontal view and normal lighting condition
works very well, the performance degenerates sharply in extreme conditions.
Recently there are many work dealing with pose and illumination problems,
respectively. However both the lighting and pose variation will always be
encountered at the same time. Accordingly we propose an end-to-end face
recognition method to deal with pose and illumination simultaneously based on
convolutional networks where the discriminative nonlinear features that are
invariant to pose and illumination are extracted. Normally the global structure
for images taken in different views is quite diverse. Therefore we propose to
use the 1*1 convolutional kernel to extract the local features. Furthermore the
parallel multi-stream multi-layer 1*1 convolution network is developed to
extract multi-hierarchy features. In the experiments we obtained the average
face recognition rate of 96.9% on multiPIE dataset,which improves the
state-of-the-art of face recognition across poses and illumination by 7.5%.
Especially for profile-wise positions, the average recognition rate of our
proposed network is 97.8%, which increases the state-of-the-art recognition
rate by 19%.
