TRIPLET DISTILLATION FOR DEEP FACE RECOGNITION

Convolutional neural networks (CNNs) have achieved great successes in face recognition, which unfortunately comes at the cost of massive computation and storage consumption. Many compact face recognition networks are thus proposed to resolve this problem, and triplet loss is effective to further improve the performance of these compact models. However, it normally employs a fixed margin to all the samples, which neglects the informative similarity structures between different identities. In this paper, we borrow the idea of knowledge distillation and define the informative similarity as the transferred knowledge. Then, we propose an enhanced version of triplet loss, named triplet distillation, which exploits the capability of a teacher model to transfer the similarity information to a student model by adaptively varying the margin between positive and negative pairs. Experiments on the LFW, AgeDB and CPLFW datasets show the merits of our method compared to the original triplet loss.