Abstract: When there is high-level illumination change or occlusion in a facial image,the feature maps produced in PCANet will mainly concentrate on the vicinity of 0 and thus lose redundancy in some extent.In order to enhance the ability of PCANet against nois,a local sphere normalization(LSN) method was proposed and embedded into the convolutional layers of PCANet.LSN embedding helps improves the redundancy of PCANet feature maps.The experiments on UMBDB and AR show that the improved PCANet is more redundant and robust,and has better recognition perfor-mances than the original PCANet.One important finding is that directly imposing LSN on the input image will lead to unstable recognition performance,while the feature redundancy should be improved steadily by LSN.

Key words: Face recognition,Feature extraction,PCANet,Local sphere normalization