RobustFace: Adaptive Mining of Noise and Hard Samples for Robust Face Recognitions

While margin-based deep face recognition models, such as ArcFace and AdaFace, have achieved remarkable successes over recent years, they may suffer from degraded performances when encountering training sets corrupted with noises. This is often inevitable when massively large scale datasets need to be dealt with, yet it remains difficult to construct clean enough face datasets under these circumstances. In this paper, we propose a robust deep face recognition model, RobustFace, by combining the advantages of margin-based learning models with the strength of mining-based approaches to effectively mitigate the impact of noises during trainings. Specifically, we introduce a noise-adaptive mining strategy to dynamically adjust the emphasis balance between hard and noise samples by monitoring the model's recognition performances at the batch level to provide optimization-oriented feedback, enabling direct training on noisy datasets without the requirement of pre-training. Extensive experiments validate that our proposed RobustFace achieves competitive performances in comparison with the existing SoTA models when trained with clean datasets. When trained with both real-world and synthetic noisy datasets, RobustFace significantly outperforms the existing models, especially when the synthetic noisy datasets are corrupted with both close-set and open-set noises. While the existing baseline models suffer from an average performance drop of around 40%, under these circumstances, our proposed still delivers accuracy rates of more than 90%.