L1-2D2PCANet: a deep learning network for face recognition

Abstract

We propose a deep learning network L1-2D2PCANet for face recognition, which is based on L1-norm-based two-dimensional principal component analysis (L1-2DPCA). In our network, the role of L1-2DPCA is to learn the filters of multiple convolution layers. After the convolution layers, we deploy binary hashing and blockwise histogram for pooling. We test our network on some benchmark facial datasets, including Yale, AR face database, extended Yale B, labeled faces in the wild-aligned, and Face Recognition Technology database with the convolution neural network, PCANet, 2DPCANet, and L1-PCANet as comparison. The results show that the recognition performance of L1-2D2PCANet 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. © 2019 SPIE and IS&T.