@Article{JFBI-8-293,
author = {Si Gao, Xiaodong Zhu, Yuanning Liu, Fei He and Guang Huo},
title = {A Quality Assessment Method of Iris Image Based on Support Vector Machine},
journal = {Journal of Fiber Bioengineering and Informatics},
year = {2015},
volume = {8},
number = {2},
pages = {293--300},
abstract = {The quality of iris image is one of the key factors in
uences the performance of iris pattern recognition.
Based on the existing quality assessment measures of iris image, and in consideration of the most
prominent factors that lead recognition to fail, we firstly put forward iris rotation which is a new quality
assessment measure. Then iris rotation, iris visibility, iris eccentricity and iris definition are together as
quality assessment measures of iris image and the quality assessment of iris image is done by Support
Vector Machine (SVM) classifier. The experiment results express that the method we propose can select
the images with good quality and has strong predictability for the performance of iris pattern recognition.},
issn = {2617-8699},
doi = {https://doi.org/10.3993/jfbim00114},
url = {http://global-sci.org/intro/article_detail/jfbi/4709.html}
}