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.
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