基于轻量型网络的口罩遮挡人脸识别方法

由于口罩的遮挡会大幅降低人脸可供识别的特征，使得之前提出的人脸识别算法在现有外部环境下的识别性能大幅下降。因此，针对现有人脸识别技术在当前应用场景中的不足，本研究采用MobileNet v2轻量级卷积神经网络替换InceptionResNet-v1网络作为骨干网络对FaceNet人脸识别方法进行了改进，在简化模型参数的同时提高了模型的运算速度，并且在MobileNet V2网络中引入一种轻量型的混合注意力模块，同时将Softmax Loss与Triplet Loss加权融合作为网络模型的联合损失函数，通过调整权值达到最优后作为损失函数进行训练，提高网络的识别准确率。实验结果表明：本研究所提出的人脸识别网络在进行口罩遮挡人脸识别时，识别准确率达到92.1%,较原有人脸识别网络有大幅提升，同时识别速度也明显优于原有网络。

Mask occlusion face recognition method based on lightweight network

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.