Volume 8, Issue 2
A Quality Assessment Method of Iris Image Based on Support Vector Machine

Si Gao, Xiaodong Zhu, Yuanning Liu, Fei He & Guang Huo

Journal of Fiber Bioengineering & Informatics, 8 (2015), pp. 293-300.

Published online: 2015-08

Preview Purchase PDF 0 1838
Export citation
  • 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.

  • Keywords

Iris Recognition Quality Assessment Iris Rotation SVM Classifier

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{JFBI-8-293, author = {}, 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} }
TY - JOUR T1 - A Quality Assessment Method of Iris Image Based on Support Vector Machine JO - Journal of Fiber Bioengineering and Informatics VL - 2 SP - 293 EP - 300 PY - 2015 DA - 2015/08 SN - 8 DO - http://doi.org/10.3993/jfbim00114 UR - https://global-sci.org/intro/article_detail/jfbi/4709.html KW - Iris Recognition KW - Quality Assessment KW - Iris Rotation KW - SVM Classifier AB - 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.
Si Gao, Xiaodong Zhu, Yuanning Liu, Fei He & Guang Huo. (2019). A Quality Assessment Method of Iris Image Based on Support Vector Machine. Journal of Fiber Bioengineering and Informatics. 8 (2). 293-300. doi:10.3993/jfbim00114
Copy to clipboard
The citation has been copied to your clipboard