中圖分類號： U491.1文獻標識碼： ADOI： 10.19358/j.issn.2096-5133.2020.06.003
引用格式： 趙國順，方建安，瞿斌杰，等. 基于頻域注意力時空卷積網絡的步態識別方法[J].信息技術與網絡安全，2020，39(6)：13-18.

Gait recognition method based on frequency domain attention spatio-temporal convolutional network

Abstract： In order to solve the problems of redundant gait information, uneven distribution of feature importance, and difficulty in learning the spatiotemporal features of the gait, a spatiotemporal convolutional network based on attention in the frequency domain was proposed for gait recognition. In the experiment, the spatial and temporal characteristics of three-dimensional convolutional network(C3D) learning were improved. At the same time, a frequency-domain attention convolution operation was proposed, which not only reduced redundant calculations, but also adjusted the attention and improved the learning effect. The network firstly divides the gait information into five groups, then extracts the spatiotemporal features through improved convolution, and finally classifies them through the Softmax layer. Tested in the CASIA dataset B of the Chinese University of Science and Technology, the accuracy rates in the Bag state and Coat state are 88.5% and 92.8% respectively, which are about 3% higher than traditional deep convolutional networks(Deep CNN). At the same time, attention is redistributed in network learning，the accuracy rate of each angle is increased by about 2% on average.

Key words : frequency domain；attention；3D convolution；gait recognition；biometrics；deep learning