Face recognition has advanced considerably with the availability of large-scale labeled datasets. However, how to further improve the performance with the easily accessible unlabeled dataset remains a challenge. In this paper, we propose the novel Unknown Identity Rejection (UIR) loss to utilize the unlabeled data. We categorize identities in unconstrained environment into the known set and the unknown set. The former corresponds to the identities that appear in the labeled training dataset while the latter is its complementary set. Besides training the model to accurately classify the known identities, we also force the model to reject unknown identities provided by the unlabeled dataset via our proposed UIR loss. In order to 'reject' faces of unknown identities, centers of the known identities are forced to keep enough margin from centers of unknown identities which are assumed to be approximated by the features of their samples. By this means, the discriminativeness of the face representations can be enhanced. Experimental results demonstrate that our approach can provide obvious performance improvement by utilizing the unlabeled data.

本文提出“未知身份拒绝”（UIR）损失，通过利用未标记的数据进一步提高人脸识别性能。对于被归类于未受约束的环境中的身份，我们将其分为已知集合和未知集合。除了训练模型以准确分类已知身份，我们还通过所提出的UIR损失使模型拒绝未标记数据提供的未知身份。利用所提出的方法，可以增强人脸表示的区分度，实验结果表明，我们的方法可以明显提高利用未标记数据的人脸识别性能。

未知身份拒绝损失: 利用无标签数据进行人脸识别