Learning to resolve uncertainties for large-scale face recognition

•We propose a ranking loss regularization for large-scale face recognition by reducing uncertainties during the training phase.•This work studies the effect of relabeling mechanism by integrating ranking loss with the ArcFace loss function.•This work demonstrates the effectiveness of the proposed method through experiments on public face recognition benchmarks.