Distilled light GaitSet: Towards scalable gait recognition

摘要

Gait recognition has made significant progress recently. However, most of existing methods utilize complicated neural networks, which lead to high computation cost. In this paper, a lightweight model named Distilled Light GaitSet (DLGS) is proposed for efficient gait recognition. More specifically, a lightweight CNN is designed for efficient computation, and a Joint Knowledge Distillation algorithm is proposed to boost the accuracy of the simplified model. Extensive experiments on the CASIA-B dataset and the OUMVLP dataset show that the proposed DLGS can reduce the number of parameters and computation cost significantly while achieving the state-of-the-art performance. (c) 2022 Elsevier B.V. All rights reserved.