融合Transformer和CNN的轻量级人脸识别算法OA北大核心CSTPCD

With the development of deep learning,convolutional neural networks have become the mainstream approach for face recognition(FR)by gradually expanding the receptive field through stacking convolutional layers to integrate local features.However,this approach suffers from the drawbacks of neglecting global semantic information of faces and lacking attention to important facial features,resulting in low recognition accuracy.Additionally,the stacking of a large number of parameters and layers poses challenges for deploying the network on resource-constrained devices.Therefore,a highly lightweight face recognition algorithm called gcsamTfaceNet is proposed,which combines Transformer and CNN.Firstly,a depthwise separable convolution is used to construct the backbone network in order to reduce the parameter count of the algorithm.Secondly,a channel-spatial attention mechanism is introduced to optimize the selection of features in both the channel and spatial domains,thereby improving the attention given to important facial regions.Building upon this,the Transformer module is integrated to capture the global semantic information of the feature maps,overcoming the limitations of convolutional neural networks in modeling long-range semantic dependencies and enhancing the algorithm's ability to perceive global features.The gcsamTfaceNet,with a parameter count of only 6.5 × 105,is evaluated on nine validation datasets including LFW,CA-LFW,CP-LFW,CFP-FP,CFP-FF,AgeDB-30,VGG2-FP,IJB-B,and IJB-C.It achieves average accuracies of 99.67％,95.60％,89.32％,93.67％,99.65％,96.35％,93.36％,89.43％,and 91.38％on these datasets,respectively.This demonstrates a good balance between parameter count and performance.