TY - JOUR T1 - A Quality Assessment Method of Iris Image Based on Support Vector Machine AU - Si Gao, Xiaodong Zhu, Yuanning Liu, Fei He & Guang Huo 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 and Guang Huo. (2015). 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