基于GA-SVM模型的虹膜质量评估方法

针对虹膜图像质量评价过程中存在的如何选取适量的评价因子、 如何降低评价因子的计算量、 如何对评价因子进行有效融合等问题, 提出一种基于遗传算法支持向量机（GA-SVM）模型和多测度评价指标的虹膜图像质量评估方法. 首先对虹膜图像进行清晰度质量评价, 粗略筛除模糊图像； 然后选用4个评价指标, 利用GA-SVM模型对评价指标值进行有效融合, 以综合评价虹膜图像质量； 最后将该方法在吉林大学第六代虹膜库中进行验证, 并与其他经典评价方法进行对比. 实验结果表明, 该方法能提高可用虹膜存活率, 并达到较好的识别精度, 同时提升系统运行速度.

Iris Quality Evaluation Method Based on GA-SVM Model

Aiming at the problems existing in the process of iris quality evaluation, such as how to select appropriate evaluation factors, how to reduce the calculation amount of evaluation factors, and how to effectively integrate evaluation factors, we proposed an iris image quality evaluation method based on genetic algorithm-support vector machine (GA-SVM) model and multi-measure evaluation indexes. Firstly, the definition quality of iris images was evaluated, and fuzzy images were roughly screened out. Secondly, four evaluation indexes were selected and GA-SVM model was used to effectively fuse the evaluation index values to comprehensively evaluate the quality of iris images. Finally, the method was verified in JLU-6.0 iris library and compared with other classical evaluation methods. Experimental results show that this method can improve the survival rate of available iris images, achieve better recognition accuracy, and improve the running speed of the system.