Distilled light GaitSet: Towards scalable gait recognition.

• A lightweight CNN is designed for efficient gait recognition. • A joint knowledge distillation algorithm is proposed for boosting the performance of the simplified model. • Extensive experiments on two public datasets demonstrate the effectiveness of the proposed method. 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 OU-MVLP dataset show that the proposed DLGS can reduce the number of parameters and computation cost significantly while achieving the state-of-the-art performance. [ABSTRACT FROM AUTHOR]