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Even though face recognition in frontal view and normal lighting conditionworks 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 beencountered at the same time. Accordingly we propose an end-to-end facerecognition method to deal with pose and illumination simultaneously based onconvolutional networks where the discriminative nonlinear features that areinvariant to pose and illumination are extracted. Normally the global structurefor images taken in different views is quite diverse. Therefore we propose touse the 1*1 convolutional kernel to extract the local features. Furthermore theparallel multi-stream multi-layer 1*1 convolution network is developed toextract multi-hierarchy features. In the experiments we obtained the averageface recognition rate of 96.9% on multiPIE dataset,which improves thestate-of-the-art of face recognition across poses and illumination by 7.5%.Especially for profile-wise positions, the average recognition rate of ourproposed network is 97.8%, which increases the state-of-the-art recognitionrate by 19%.

Local feature hierarchy for face recognition across pose and illumination