A lightweight face recognition algorithm based on MobileNet is proposed in this paper to address limited computational power and storage resources in patient recognition by mobile nursing robots. Firstly, MobileNet-v2 is used as the backbone network, and redundant Block blocks are pruned to reduce the number of parameters. Secondly, ShuffleNet's spatially separable convolution is introduced in the residual blocks to increase network parallelism. Finally, the original Softmax loss function is replaced with an improved ArcFace loss function, which includes a Taylor expansion in the Target logit value, to enhance network constraint and achieve better separability. Experimental results show that the improved face recognition algorithm achieves a combined recognition rate of 97% and a combined average speed of 0.725 s, fulfilling the goal of designing a lightweight and efficient deep learning network.
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