Author(s)

Xinzheng Xu^1,2*, Meng Du¹, Huanxiu Guo¹, Jianying Chang¹, Xiaoyang Zhao¹

¹School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
²Engineering Research Center of Mining Digital Ministry of Education, China University of Mining and Technology, Xuzhou, China.

Face recognition is a kind of biometric technology that recognizes identities through human faces. At first, the speed of machine recognition of human faces was slow and the accuracy was lower than manual recognition. With the rapid development of deep learning and the application of Convolutional Neural Network (CNN) in the field of face recognition, the accuracy of face recognition has greatly improved. FaceNet is a deep learning framework commonly used in face recognition in recent years. FaceNet uses the deep learning model GoogLeNet, which has a high accuracy in face recognition. However, its network structure is too large, which causes the FaceNet to run at a low speed. Therefore, to improve the running speed without affecting the recognition accuracy of FaceNet, this paper proposes a lightweight FaceNet model based on MobileNet. This article mainly does the following works: Based on the analysis of the low running speed of FaceNet and the principle of MobileNet, a lightweight FaceNet model based on MobileNet is proposed. The model would reduce the overall calculation of the network by using deep separable convolutions. In this paper, the model is trained on the CASIA-WebFace and VGGFace2 datasets, and tested on the LFW dataset. Experimental results show that the model reduces the network parameters to a large extent while ensuring the accuracy and hence an increase in system computing speed. The model can also perform face recognition on a specific person in the video.

Face Recognition, Deep Learning, FaceNet, MobileNet

Xu, X. , Du, M. , Guo, H. , Chang, J. and Zhao, X. (2021) Lightweight FaceNet Based on MobileNet. International Journal of Intelligence Science, 11, 1-16. doi: 10.4236/ijis.2021.111001.