As the mask will greatly reduce the features available for face recognition, the recognition performance of the previously proposed face recognition algorithm will be greatly reduced in the existing external environment. Therefore, given the shortcomings of the existing face recognition technology in the current application scenarios, this study uses MobileNet v2 lightweight convolutional neural network to replace the Inceptionresnet-V1 network as the backbone network to improve the FaceNet face recognition method, which simplifies the model parameters and improves the operation speed of the model. In addition, a lightweight mixed attention module is introduced into the Mobilenet v2 network, and the weighted fusion of Softmax Loss and Triplet Loss is used as the joint Loss function of the network model, which is trained as the Loss function after the adjustment of weight reaches the optimal value to improve the recognition accuracy of the network. The experimental results show that the face recognition network proposed in this study achieves 92.1% recognition accuracy in face mask masking, which is significantly improved compared with the original face recognition network, and the recognition speed is also significantly better than the original network.