Paper Info
Title
An Automated Video-Based System for Iris Recognition
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. The selection method's 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 feasible. Furthermore, our study showed that in practice an automated best image selection is nearly equivalent to human selection.
Year
DOI
Venue
2009
10.1007/978-3-642-01793-3_117
ICB
Keywords
Field
DocType
human selection,selection method,automated video-based system,iris template,iris image,iris region,iris recognition,off-angle video imagery,iris pattern,best quality iris image,automated best image selection,image quality
Iris recognition,Computer vision,Normalization (statistics),Pattern recognition,Computer science,Artificial intelligence,Biometrics,Eye detection,Image selection,Encoding (memory)
Conference
Volume
ISSN
Citations 
5558
0302-9743
12
PageRank 
References 
Authors
0.82
9
3
Name
Order
Citations
PageRank
Yooyoung Lee1392.65
P. Jonathon Phillips29209801.62
Ross J. Micheals331332.03