Research on Recognition of Faces with Masks Based on Improved Neural Network

Author:

Zhang Song1ORCID,Sun Jiandong1,Kang Jie1,Wang Shaoqiang2

Affiliation:

1. The Affiliated Hospital of Qingdao University, Qingdao 266000, China

2. School of Computer and Communication Engineering, China University of Petroleum (East of China), Qingdao 266000, China

Abstract

Background. At present, the new crown virus is spreading around the world, causing all people in the world to wear masks to prevent the spread of the virus. Problem. People with masks have found a lot of trouble for face recognition. Finding a feasible method to recognize faces wearing masks is a problem that needs to be solved urgently. Method. This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network and the integrated SE-Net and DenseNet network and introduces deformable convolution. Conclusion. Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of face recognition and detection with masks to a certain extent.

Publisher

Hindawi Limited

Subject

Health Informatics,Biomedical Engineering,Surgery,Biotechnology

Reference18 articles.

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Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Open Set Masked Face Identification system;2023 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI);2023-12-10

2. An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection;Lecture Notes in Computer Science;2022

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