Deep Metric Learning Based On Center-Ranked Loss for Gait Recognition

Gait information has gradually attracted people’s attention duing to its uniqueness. Methods based on deep metric learning are successfully utlized in gait recognition tasks. However, most of the previous studies use losses which only consider a small number of samples in the mini-batch, such as Triplet loss and Quadruplet Loss, which is not conducive to the convergence of the model. Therefore, in this paper, a novel loss named Center-ranked is proposed to integrate all positive and negative samples information. We also propose a simple model for gait recognition tasks to verify the validity of the loss. Extensive experiments on two challenging datasets CASIA-B and OU-MVLP demonstrate the superiority and effectiveness of our proposed Center-ranked loss and model.