Abstract
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.
Highlights
With the advent of the era of big data, artificial intelligence has been developing more rapidly
2) Fast-FaceNet was applied to video face recognition to improve the recognition rate while ensuring a certain recognition accuracy rate
In order to verify the model proposed in this paper, the CASIA WebFace dataset and the VGGFace2 dataset are used to train the proposed Fast-FaceNet model, and the trained model is tested with the LFW dataset
Summary
With the advent of the era of big data, artificial intelligence has been developing more rapidly. The deep neural network model has been widely used in computer vision tasks such as image classification and face detection [12], and has achieved very good results. FaceNet is a general face recognition system that maps images to Euclidean space through deep neural networks. Taigman et al proposed a multi-stage method to align the face with a general 3D model They trained a multi-category network that can perform facial recognition on more than 4,000 identities. The authors conducted experiments on the proposed twin network, in which they optimized the L1 distance between two facial features Their best performance on LFW comes from the collection of three networks using different arrangements and color channels, using nonlinear SVM to combine the prediction distances of these networks (nonlinear SVM prediction based on χ2 kernel), through semantic and visual similarity Ranking images.
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