Deep Representation Learning With Feature Augmentation for Face Recognition

Deep Convolutional Neural Networks (DCNN) significantly improve the performance of many computer vision tasks, such as classification, detection, and semantic segmentation. The ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under open-set protocol, but the current algorithm still has the open problem of implementing the criterion. In this paper, we present a feature augmentation network for the IARPA Janus Benchmark C (IJB-C) on a small CNN. The proposed feature enhancement method is used to approximate the identity features, and the original features are augmented by a small automatic encoder-decoder which can be quickly run in an embedded system with limited resources and obtains similar accuracy to a large backbone CNN network.