One-Shot Face Recognition with Feature Rectification via Adversarial Learning

One-shot face recognition has attracted extensive attention with the ability to recognize persons at just one glance. With only one training sample which cannot represent intra-class variance adequately, one-shot classes have poor generalization ability, and it is difficult to obtain appropriate classification weights. In this paper, we explore an inherent relationship between features and classification weights. In detail, we propose feature rectification generative adversarial network (FR-GAN) which is able to rectify features closer to corresponding classification weights considering existing classification weights information. With one model, we achieve two purposes: without fine-tuning via back propagation as previous CNN approaches which are time consuming and computationally expensive, FR-GAN can not only (1) generate classification weights for new classes using training data, but also (2) achieve more discriminative test feature representation. The experimental results demonstrate the remarkable performance of our proposed method, as in MS-Celeb-1M one-shot benchmark, our method achieves 93.12% coverage at 99% precision with the introduction of novel classes and remains a high accuracy at 99.80% for base classes, surpassing most of the previous approaches based on fine-tuning.