In the field of face recognition, a model learns to distinguish millions of face images with fewer dimensional embedding features, and such vast information may not be properly encoded in the conventional model with a single branch. We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature. The proposed method provides self-distributed labels that balance the number of samples belonging to each group without additional human annotations, and learns the group-aware representations that can narrow down the search space of the target identity. We prove the effectiveness of the proposed method by showing extensive ablation studies and visualizations. All the components of the proposed method can be trained in an end-to-end manner with a marginal increase of computational complexity. Finally, the proposed method achieves the state-of-the-art results with significant improvements in 1:1 face verification and 1:N face identification tasks on the following public datasets: LFW, YTF, CALFW, CPLFW, CFP, AgeDB-30, MegaFace, IJB-B and IJB-C.

提出了一种名为GroupFace的面部识别特定的网络结构，利用多个分组感知表示同时提高嵌入特征的质量，并通过提供自分布标签来证明其有效性，同时学习可以缩小目标身份搜索空间的分组感知表示，最终在多个公共数据集上实现了1:1面部验证和1:N面部识别任务的显着改进。

GroupFace: 学习潜在群体及构建基于群体的人脸识别表示