IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7952152.html
   My bibliography  Save this article

Efficient Iris Localization via Optimization Model

Author

Listed:
  • Qi Wang
  • Zhipeng Liu
  • Shu Tong
  • Yuqi Yang
  • Xiangde Zhang

Abstract

Iris localization is one of the most important processes in iris recognition. Because of different kinds of noises in iris image, the localization result may be wrong. Besides this, localization process is time-consuming. To solve these problems, this paper develops an efficient iris localization algorithm via optimization model. Firstly, the localization problem is modeled by an optimization model. Then SIFT feature is selected to represent the characteristic information of iris outer boundary and eyelid for localization. And SDM (Supervised Descent Method) algorithm is employed to solve the final points of outer boundary and eyelids. Finally, IRLS (Iterative Reweighted Least-Square) is used to obtain the parameters of outer boundary and upper and lower eyelids. Experimental result indicates that the proposed algorithm is efficient and effective.

Suggested Citation

  • Qi Wang & Zhipeng Liu & Shu Tong & Yuqi Yang & Xiangde Zhang, 2017. "Efficient Iris Localization via Optimization Model," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-9, December.
    • Handle: RePEc:hin:jnlmpe:7952152
    DOI: 10.1155/2017/7952152
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2017/7952152.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2017/7952152.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2017/7952152?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:7952152. 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.