IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i2d10.1007_s13198-021-01476-2.html
   My bibliography  Save this article

Face recognition robot system based on intelligent machine vision image recognition

Author

Listed:
  • Min Cao

    (Fujian Jiangxia University)

Abstract

Face detection and key part extraction are the prerequisites for face recognition, and the goal is to obtain the current data set or image set face attribute features. This paper combines intelligent machine vision technology to construct a face recognition robot system. In order to solve the influence of noise environment and low resolution on AU recognition, this paper adopts a facial feature extraction method based on AU area. Moreover, in order to reduce the influence of noise factors on feature extraction, this paper preprocesses the image and combines image denoising and edge extraction methods to eliminate background. In addition, this paper constructs an intelligent image recognition algorithm based on the requirements of facial feature recognition, and uses this algorithm as the core algorithm of the robot. Finally, this paper designs experiments to verify the system. The experimental research results show that the face recognition robot system based on intelligent machine vision image recognition constructed in this paper has a good recognition effect.

Suggested Citation

  • Min Cao, 2023. "Face recognition robot system based on intelligent machine vision image recognition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 708-717, April.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:2:d:10.1007_s13198-021-01476-2
    DOI: 10.1007/s13198-021-01476-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-021-01476-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13198-021-01476-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ijsaem:v:14:y:2023:i:2:d:10.1007_s13198-021-01476-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.