The iris feature point averaging method in student eye gaze tracking

Fangfang Yang, Yaping Dai, Lei Wang, Zhiyang Jia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In order to solve the problems that the infrared camera must be used to illuminate the human eye to obtain the iris image, and because the eyelid covers, the iris image can not be extracted completely, which cause the failure of tracking the iris accurately. In this paper, the Average Feature Point (AFP) is proposed to locate iris center. This method uses the camera of notebook to take human photos, recognizes face by Haar features, and further intercepts the human eye region; and the First Valley Method (FVM) is used to select the appropriate iris binarization threshold by analyzing the eye gray histogram; after that, use the feature of the maximum connected domain to eliminate the noise of the image; finally AFP is used to locate the center of the iris. A large number of experiments have been conducted on different states of human images, the results showed that AFP improved the accuracy of locating iris center compared with the traditional Hough method.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages5520-5524
Number of pages5
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Average Feature Point
  • Eye Gray Histogram
  • First Valley Method
  • Iris Location

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Yang, F., Dai, Y., Wang, L., & Jia, Z. (2018). The iris feature point averaging method in student eye gaze tracking. In X. Chen, & Q. Zhao (Eds.), Proceedings of the 37th Chinese Control Conference, CCC 2018 (pp. 5520-5524). Article 8482573 (Chinese Control Conference, CCC; Vol. 2018-July). IEEE Computer Society. https://doi.org/10.23919/ChiCC.2018.8482573