Convolutional neural networks (CNN) have achieved outstanding face recognition (FR) performance with increasing large-scale face datasets. With face dataset size grown, noisy data will inevitably increase, undoubtedly bringing difficulties to data cleaning. In this paper, the probability that the sample belongs to noise can be determined based on the cosine distance (cos$\theta$) of normalized angle center and face feature vector in the margin-based loss functions. According to this finding, we propose a two-step learning method integrated into the loss function. The new proposed directional margin loss function combines the noise probability with the label as the supervision information. Experiments show that our method can tolerate noisy data and get high FR accuracy when the training datasets mix with more than 30% noise. Our approach can also achieve a great result of 79.33% in MegaFace challenge one using a noisy training dataset.

随着大规模人脸数据集的增加，卷积神经网络 (CNN) 取得了出色的人脸识别 (FR) 性能。随着人脸数据集规模的增长，噪声数据不可避免地会增加，这无疑给数据清洗带来了困难。本文可以根据余弦距离（cos$\theta$) 的归一化角度中心和基于边缘的损失函数中的人脸特征向量。根据这一发现，我们提出了一种集成到损失函数中的两步学习方法。新提出的方向边际损失函数将噪声概率与标签相结合作为监督信息。实验表明，当训练数据集与超过 30% 的噪声混合时，我们的方法可以容忍噪声数据并获得高 FR 精度。我们的方法还可以使用嘈杂的训练数据集在 MegaFace 挑战赛中取得 79.33% 的出色成绩。