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In the field of face recognition, a model learns to distinguish millions offace images with fewer dimensional embedding features, and such vastinformation may not be properly encoded in the conventional model with a singlebranch. We propose a novel face-recognition-specialized architecture calledGroupFace that utilizes multiple group-aware representations, simultaneously,to improve the quality of the embedding feature. The proposed method providesself-distributed labels that balance the number of samples belonging to eachgroup without additional human annotations, and learns the group-awarerepresentations that can narrow down the search space of the target identity.We prove the effectiveness of the proposed method by showing extensive ablationstudies and visualizations. All the components of the proposed method can betrained in an end-to-end manner with a marginal increase of computationalcomplexity. Finally, the proposed method achieves the state-of-the-art resultswith significant improvements in 1:1 face verification and 1:N faceidentification tasks on the following public datasets: LFW, YTF, CALFW, CPLFW,CFP, AgeDB-30, MegaFace, IJB-B and IJB-C.

GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition