[1]GU Fengwei,LU Jun,XIA Guihua.Face recognition method based on facenet Pearson discrimination network[J].CAAI Transactions on Intelligent Systems,2022,17(1):107-115.[doi:10.11992/tis.202104008]
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Face recognition method based on facenet Pearson discrimination network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 17 Number of periods: 2022 1 Page number: 107-115 Column: 学术论文—智能系统 Public date: 2022-01-05
Title:
Face recognition method based on facenet Pearson discrimination network
Author(s):
GU Fengwei1; 2;  LU Jun1; 2;  XIA Guihua1; 2
1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. Key Laboratory of Intelligent Technology and Application of Marine Equipment, Harbin Engineering University, Harbin, 150001, China
Keywords:
unrestricted scene; face recognition; facenet; multi-task cascaded convolutional neural network; face detection; Pearson correlation coefficient; euclidean distance; face dataset
CLC:
TP391.41
DOI:
10.11992/tis.202104008
Abstract:
In unrestricted scenes, there are problems such as illumination, occlusion, and pose changes, which seriously affect the performance and accuracy of the face recognition model. This paper improves facenet to solve this problem, and proposes a facenetPDN method based on facenet Pearson discriminant network. Firstly, the deep convolutional neural network in facenetPDN is constructed, and the multi-task cascaded convolutional neural network is fused on the front-end of facenet to detect and extract the target face. Then, the facial depth feature information is extracted through the deep convolutional neural network, and the Pearson correlation coefficient discrimination module is used to replace the Euclidean distance discrimination module in the facenet algorithm to realize the facial depth feature discrimination. Finally, CASIA-WebFace and CASIA-FaceV5 face datasets are used to train the network. The trained model is tested and evaluated on the LFW and celeA face datasets to prove effectiveness of the method in this paper, and a comparative analysis is performed. The experimental results show that the accuracy of the improved facenetPDN method is 1.34% higher than that of the original method as a whole, and the accuracy of the model after training in the fusion training dataset is improved by 0.78%. The algorithm has excellent robustness and generalization ability, which can realize multi-ethnic face recognition, and has a good recognition effect on face targets in unrestricted scenes.
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