  In this paper, we propose a novel deep learning network L1-(2D)2PCANet for
face recognition, which is based on L1-norm-based two-directional
two-dimensional principal component analysis (L1-(2D)2PCA). In our network, the
role of L1-(2D)2PCA is to learn the filters of multiple convolution layers.
After the convolution layers, we deploy binary hashing and block-wise histogram
for 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 as
comparison. The results show that the recognition performance of L1-(2D)2PCANet
in all tests is better than baseline networks, especially when there are
outliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust to
outliers and changes of the training images.
