Training triplet networks with large-scale data is challenging in face recognition. Due to the number of possible triplets explodes with the number of samples, previous studies adopt the online hard negative mining(OHNM) to handle it. However, as the number of identities becomes extremely large, the training will suffer from bad local minima because effective hard triplets are difficult to be found. To solve the problem, in this paper, we propose training triplet networks with subspace learning, which splits the space of all identities into subspaces consisting of only similar identities. Combined with the batch OHNM, hard triplets can be found much easier. Experiments on the large-scale MS-Celeb-1M challenge with 100K identities demonstrate that the proposed method can largely improve the performance. In addition, to deal with heavy noise and large-scale retrieval, we also make some efforts on robust noise removing and efficient image retrieval, which are used jointly with the subspace learning to obtain the state-of-the-art performance on the MS-Celeb-1M competition (without external data in Challenge1).

该研究提出了使用子空间学习训练三元组网络来改进人脸识别问题，结合小批量在线硬负样本挖掘策略，能够更容易地找到难的三元组，从而在 MS-Celeb-1M 数据集上取得了很好的表现。同时，该方法使用鲁棒的去噪和高效的图像检索，能够更好地应对大规模噪声和图像检索问题。

如何使用10万身份训练三元组网络？