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Improved PCA Face Recognition Algorithm

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Data Science (ICPCSEE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1257))

Abstract

Face recognition technology, as a biometric recognition technology, is very mature and has many applications. It achieves in-depth applications in smart campus systems, such as classroom attendance, classroom behavior analysis, and smart restaurants. Using human faces as the face data foundation, computer vision and image processing technologies are applied to research and implement face recognition. Based on the principal component analysis (PCA) theory, this paper analyzed the characteristics of face data, studied the face recognition algorithm. Considering the LBP and SVM algorithm, an improved PCA face recognition algorithm was proposed. Through comparative experiments, the results show that the proposed algorithm can improve the accuracy of face recognition.

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Acknowledgment

This paper is supported by the Ph.D. Research Initiation Fund of Nanchang Institute of Science and Technology with the Project (No. NGRCZX-18-01). It is also supported by the Science and Technology Project of Jiangxi Provincial Department of Education with the Project (No. GJJ191105).

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Authors and Affiliations

  1. Nanchang Institute of Science and Technology, Nanchang, China

    Yang Tao

  2. Guangdong University of Science and Technology, Dongguan, China

    Yuanzi He

Authors
  1. Yang Tao
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  2. Yuanzi He
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Corresponding author

Correspondence to Yang Tao .

Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Tao, Y., He, Y. (2020). Improved PCA Face Recognition Algorithm. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_44

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  • DOI: https://doi.org/10.1007/978-981-15-7981-3_44

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-7980-6

  • Online ISBN: 978-981-15-7981-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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