Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

An Automated Video-Based System For Iris Recognition

Published

Author(s)

Yooyoung Lee, P. Jonathon Phillips, Ross J. Micheals

Abstract

We have successfully implemented a Video-based Automated System for Iris Recognition (VASIR), evaluating its successful performance on the MBGC dataset. The proposed method facilitates the ultimate goal of automatically detecting an eye area, extracting eye images, and selecting the best quality iris image from video frames. its performance is evaluated by comparing it to the selection performed by humans. Masek's algorithm was adapted to segment and normalize the iris region. Encoding the iris pattern and then completing the matching followed this stage. The iris templates from video images were compared to pre-existing still iris images for the purpose of the verification. This experiment has shown that even under varying illumination conditions, low quality, and off-angle video imagery, that iris recognition is nevertheless feasible. Furthermore, our study showed that in practice an automated best image selection is nearly equivalent to human selection.
Proceedings Title
ICB2009 (LNCS volume by Springer)
Conference Dates
June 2-5, 2009
Conference Location
Sassari, IT
Conference Title
3rd IAPR / IEEE International Conference on Biometrics

Keywords

Biometrics, Iris Recognition, Eye Detection, Image Quality Measurement, VASIR, Iris Segmentation.

Citation

Lee, Y. , Phillips, P. and Micheals, R. (2009), An Automated Video-Based System For Iris Recognition, ICB2009 (LNCS volume by Springer), Sassari, IT, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=901513 (Accessed June 15, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 1, 2009, Updated October 12, 2021