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GroupFace: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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 groupaware representations, simultaneously, to improve the quality of the embedding feature. The proposed method provides self-distributed labels that balance thedoi:10.1109/cvpr42600.2020.00566 dblp:conf/cvpr/KimPRS20 fatcat:si4id6eelvdprdj5harsy7vz5e