Facial recognition is a category of biometric security, used widely in various industries where we identify and authenticate an individuals identity using their face. In the modern deep learning era, face recognition datasets are playing a significant role in achieving state-of-the-art accuracy by acquiring and training millions of face images. Annotating such a large-scale face recognition dataset is challenging due to low-quality face images, and incorrect annotations unknowingly made by annotators. Training a deep learning model with such uncertainties leads to deep model overfitting on noisy uncertain samples and degradation of the discriminative ability of the model. To address these issues, we propose a simple yet effective uncertainty learning network that efficiently reduces over-fitting caused by uncertain face images. More specifically, our FC module weights each sample in the mini-batch at the decision layer, and relabeling mechanism carefully modify the labels of incorrect samples in the mini- batch. Results on IJB-B, IJB-C, LFW, AgeDB30, CFP-FP, CALFW and CPLFW public datasets demonstrate that our approach achieves state-of-the-art performance

面部识别是生物识别安全的一个类别，广泛用于各个行业，我们使用他们的面部识别和验证个人身份。在现代深度学习时代，人脸识别数据集通过获取和训练数百万张人脸图像，在实现最先进的准确性方面发挥着重要作用。由于低质量的人脸图像以及注释者在不知不觉中做出的错误注释，对如此大规模的人脸识别数据集进行注释具有挑战性。训练具有这种不确定性的深度学习模型会导致深度模型对嘈杂的不确定样本过度拟合，并降低模型的判别能力。为了解决这些问题，我们提出了一个简单而有效的不确定性学习网络，可以有效地减少由不确定的人脸图像引起的过度拟合。进一步来说，我们的 FC 模块在决策层对 mini-batch 中的每个样本进行加权，并且重新标记机制会仔细修改 mini-batch 中不正确样本的标签。IJB-B、IJB-C、LFW、AgeDB30、CFP-FP、CALFW 和 CPLFW 公共数据集的结果表明，我们的方法实现了最先进的性能