Gait recognition based on margin sample mining loss

Gait recognition, as one of the new biometric techniques, mainly judges and identifies a target pedestrian through its walking posture. Gait recognition is effective at long distances, difficult to camouflage and requires no contact or cooperation with the target pedestrian. However, the accuracy of gait recognition is affected by external factors, such as the shooting angle of the video, the clothes and bags worn by the target. In this paper, we solve the above problems based on two aspects. Firstly, a gait recognition method based on MSM Loss is proposed. In this way we are able to extract more discriminative spatio-temporal features; Secondly, we also introduce a new input method, which makes each input sequence more closely related, thus improving the gait recognition rate. Finally, the proposed method is verified on the CASIA-B and OU-MVLP dataset. In CASIA-B, the average recognition rate is obtained under the walking conditions of normal, with bags and with clothes. With rank-1 accuracy under LT, the method proposed in this paper can reach 96.4% under NM, 89.1% under BG and 71.2% under CL. And under the normal walking conditions, our method performs better compared with the best existing gait recognition methods. And in OU-MVLP, we get 87.5% accuracy.