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In this paper, we propose a novel deep learning network L1-(2D)2PCANet forface recognition, which is based on L1-norm-based two-directionaltwo-dimensional principal component analysis (L1-(2D)2PCA). In our network, therole of L1-(2D)2PCA is to learn the filters of multiple convolution layers.After the convolution layers, we deploy binary hashing and block-wise histogramfor pooling. We test our network on some benchmark facial datasets YALE, AR,Extended Yale B, LFW-a and FERET with CNN, PCANet, 2DPCANet and L1-PCANet ascomparison. The results show that the recognition performance of L1-(2D)2PCANetin all tests is better than baseline networks, especially when there areoutliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust tooutliers and changes of the training images.

L1-(2D)2PCANet: A Deep Learning Network for Face Recognition