基于深度卷积神经网络的人脸识别模型虽然能够取得较高的识别精度,但是模型中存在海量的计算数据并且需要占用大量的内存资源,因此无法满足资源受限和实时性的要求。针对此问题,设计两种轻量化递归残差神经网络,该网络能够有效地融合特征图中各层之间的信息,丰富特征图的语义信息进而提高识别精度。首先对原始数据集采用MTCNN人脸检测算法进行人脸对齐和裁剪;然后将ArcFace损失函数作为监督信号,此损失函数能够使得数据集类内聚合和类间分散,有效提高模型的分类效果;最后在LFW、AgeDB和CFP-FP数据集上对模型进行验证。实验结果表明,设计的网络模型在减少大量参数的情况下可以取得较高的人脸识别精度。

Although the face recognition model based on the deep convolutional neural network can achieve high recognition accuracy, there are massive calculations in the model and a large amount of memory resources are required, which cannot meet the resource constraints and real-time requirements. To solve this problem, two lightweight recursive residual neural networks are designed, which can effectively fuse the information between the layers in the feature map, enrich the semantic information of the feature map and improve the recognition accuracy. First, the MTCNN face detection algorithm is used to face alignment and cropping on the original data set. Then, the ArcFace loss function is used as the supervision signal, this loss function can make the data set aggregation and inter-class dispersion, effectively improve the classification effect of the model. Finally, the model is verified on the LFW, AgeDB and CFP-FP datasets. Experimental results show that the designed network model can achieve high face recognition accuracy while reducing a large number of parameters.