IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/6620742.html
   My bibliography  Save this article

Facial Image Segmentation Based on Gabor Filter

Author

Listed:
  • Hong-An Li
  • Jiangwen Fan
  • Jing Zhang
  • Zhanli Li
  • Dandan He
  • Ming Si
  • Yun Zhang

Abstract

As an important part of face recognition, facial image segmentation has become a focus of human feature detection. In this paper, the AdaBoost algorithm and the Gabor texture analysis algorithm are used to segment an image containing multiple faces, which effectively reduces the false detection rate of facial image segmentation. In facial image segmentation, the image containing face information is first analyzed for texture using the Gabor algorithm, and appropriate thresholds are set with different thresholds of skin-like areas, where skin-like areas in the image’s background information are removed. Then, the AdaBoost algorithm is used to detect face regions, and finally, the detected face regions are segmented. Experiments show that this method can quickly and accurately segment faces in an image and effectively reduce the rate of missed and false detections.

Suggested Citation

  • Hong-An Li & Jiangwen Fan & Jing Zhang & Zhanli Li & Dandan He & Ming Si & Yun Zhang, 2021. "Facial Image Segmentation Based on Gabor Filter," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-7, February.
    • Handle: RePEc:hin:jnlmpe:6620742
    DOI: 10.1155/2021/6620742
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2021/6620742.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2021/6620742.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2021/6620742?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
    ---><---

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:6620742. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.