Robust Face Recognition via Adaptive Mining and Margining of Noise and Hard Samples

At present, deep face recognition models working on millions of images are confronted with the challenge that such large-scale datasets are often corrupted with noises and mislabeled identities yet most deep models are primarily designed for clean datasets. In this paper, we propose a robust deep face recognition model by exploiting the advantage of integrating the strength of margin-based learning models with the strength of mining-based approaches to effectively mitigate the impact of noises during training. By monitoring the recognition performances at a batch level to provide optimization-oriented feedback, we introduce a noise-adaptive mining strategy to dynamically adjust the emphasis balance between hard and noise samples, enabling direct training on noisy datasets without the requirement of pre-training. With a novel anti-noise loss function, learning is empowered for direct and robust training on noisy datasets yet its effectiveness over clean datasets is still preserved, sustaining effective mining of both clean and noisy samples whilst weakening its learning intensiveness over noisy samples. Extensive experiments reveal that: (i) our proposed achieves competitive performances in comparison with representative existing SoTA models when trained with clean datasets; (ii) when trained with both real-world and synthesized noisy datasets, our proposed significantly outperforms the existing models, especially when the synthesized datasets are corrupted with both close-set and open-set noises; (iii) while the existing deep models suffer from an average performance drop of around 20% over noise-corrupted large scale datasets, our proposed still delivers accuracy rates of more than 95%. Our source codes are publicly available on GitHub.