Abstract
• 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.
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