Lightweight gait recognition method based on involution neural network
The existing gait recognition methods have disadvantages such as heavy computation,slow recognition rate and easily being affected by the angle of view changes,which makes it difficult to deploy the model and reduces the accuracy of gait recognition.This paper proposes a high accuracy gait recognition method based on involution neural network to solve the above problem.Firstly,an involution neural network model based on residual network architecture and involution neural network operator is proposed,in which the model uses the entrainment layer to extract gait features to reduce model training parameters.Then,based on the involution neural network model,a joint loss function consisting of Triplet loss and traditional loss function(Softmax loss)is established.The function makes the proposed model have better recognition performance and higher recognition accuracy across view conditions.Finally,experimental verification is carried out based on CASIA-B gait dataset.The experimental results show that the number of parameters of the proposed method is only 5.04 MB,which is reduced by 53.46%compared with the residual network before modification.In addition,the proposed network has better recognition accuracy than the mainstream algorithm under the same angle of view and cross-view conditions,solving the problem of reduced gait recognition accuracy under the angle of view changes.
gait recognitioninvolution neural networkresidual networkneural network operatorinvolution layertriplet lossSoftmax lossjoint loss function
国家科技期刊平台
NSTL
万方数据
维普
