Abstract
Human face is a widely used biometric modality for verification and revealing the identity of a person. In spite of a great deal of research on face recognition, it still is a challenging issue. Recently, the outstanding performance of deep learning has attracted a good deal of research interest for face recognition. In comparison with hand-engineered features, learning-based face features have proven their superiority in encoding discriminative information. Inspired by deep learning, we introduce a simple and efficient unsupervised feature learning scheme for face recognition. This scheme employs principle component analysis (PCA), local binary pattern (LBP), and pyramid pooling. Following the architecture of a convolutional neural network, the proposed scheme contains three types of layers: convolutional, nonlinear, and pooling layers. PCA is used to learn a filter bank for the convolutional layer. This is followed by LBP operator that encodes the local texture and adds nonlinearity to the feature maps of convolutional layer, which are then pooled using spatial pyramid pooling. To corroborate the effectiveness of the scheme (which we call as PCAPool), extensive experiments were performed on challenging benchmark databases: FERET, Yale, Extended Yale B, AR, and multi-PIE. The comparison reveals that PCAPool performs better than the state-of-the-art methods.