Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 1
277-281,287

Multi feature fusion for masked face recognition based on contrastive learning

Chen Anming1
Lin Qunxiong2
Liu Weiqiang1
1. Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen Guangdong 518055, China
2. Guangdong Public Secu-rity Science & Technology Collaborative Innovation Center, Guangzhou 510050, China
DOI: 10.19734/j.issn.1001-3695.2023.06.0266

Abstract

With the development of computer vision technology and the popularization of intelligent terminals, facial recognition under mask occlusion has become an important part of character identity information recognition. The large area occlusion of masks poses great challenges to the learning of facial features. To solve this problem, this paper proposed a multi feature fusion based masked face recognition algorithm based on contrastive learning. This algorithm improved the traditional face feature vector learning loss function based on the triple relationship. It proposed a loss function based on the multi-instance relationship, which fully excavated the intra-modal and inter- modal correlation between multiple positive and negative samples of the masked face and the full face. Then, the features with high discrimination ability were learnt from the face. Meanwhile, it combined the local features such as eyebrows and eyes, as well as global features such as contours, to learn the effective feature vector representation of the masked face. This paper compared it with the benchmark algorithm on real masked face datasets and generated masked face data. The experimental results show that the proposed algorithm has higher recognition accuracy than the traditional triple loss function and feature fusion model.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.06.0266
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 1
Section: Technology of Graphic & Image
Pages: 277-281,287
Serial Number: 1001-3695(2024)01-044-0277-05

Publish History

[2023-10-09] Accepted Paper
[2024-01-05] Printed Article

Cite This Article

陈岸明, 林群雄, 刘伟强. 基于对比学习的多特征融合戴口罩人脸识别 [J]. 计算机应用研究, 2024, 41 (1): 277-281,287. (Chen Anming, Lin Qunxiong, Liu Weiqiang. Multi feature fusion for masked face recognition based on contrastive learning [J]. Application Research of Computers, 2024, 41 (1): 277-281,287. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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