生理结构先验引导下的虹膜精确分割算法

虹膜识别是一种即时有效、被广泛应用的生物技术,其相对于人脸识别、指纹识别拥有更高的安全性能。但虹膜识别系统整体性能在很大程度上受虹膜分割精度的影响。为了有效提高虹膜识别系统性能即虹膜分割精度,本文在分析虹膜生理结构特点的基础上,大量阅读了国内外相关领域文献并分析各种算法优缺点,创新性地提出了一种新的虹膜精确分割算法,打破了传统分割算法中虹膜与瞳孔为同心圆的假设;借鉴完全局部二值模式CLBP算法思想,融合图像灰度信息和结构信息,创新性地提出了形状敏感的检测算子,有效剔除了影响分割精度的两大因素:眼睑和睫毛的干扰。同时提出了分割流程,分为两部分:虹膜粗分割与精确分割,粗分割包括外轮廓与瞳孔剔除,精分割包括眼睑与睫毛剔除。最后在中科院自动化所公开虹膜数据集CASIA-IrisV3-Interval和CASIA-IrisV1上进行了一系列有关精度和运算效率的对比实验。采用本文所提出的分割算法,在公开的OSIRIS Version 4.1虹膜识别系统上进行实验,其准确率分别提高到了97.14%和98.28%,运算时长显著减少并分别达到了0.699 s与0.758 s。

An accurate iris segmentation algorithm guided by prior physiological structure

Iris recognition is an effective and widely used biotechnology, which has higher security performance than face recognition and fingerprint recognition. However, the overall performance of the recognition system is largely affected by the iris segmentation accuracy. In order to effectively improve the iris segmentation accuracy, based on the analysis of the physiological structure of iris, the literature in the relevant fields at home and abroad was reviewed, and the advantages and disadvantages of various algorithms were analyzed. A new accurate iris segmentation algorithm was proposed, which overcomes the hypothesis of concentric circles of traditional segmentation algorithms. Drawing on the idea of completed local binary patterns (CLBP) algorithm and fusing the grayscale information and structural information of the image, the shape-sensitive detection operator was proposed to effectively eliminate the two major factors that affect segmentation accuracy, i.e., the interference of eyelid and eyelashes. In addition, a segmentation process was proposed, which is divided into two parts: coarse iris segmentation and precise segmentation. Coarse segmentation includes outer contour and pupil rejection, and precise segmentation includes eyelid and eyelash rejection. Finally, a series of comparative tests were conducted to investigate accuracy and calculation efficiency on the iris datasets CASIA-IrisV3-Interval and CASIA-IrisV1 published by the Institute of Automation, Chinese Academy of Sciences. After using the proposed segmentation algorithm, the accuracy on the OSIRIS Version 4.1 iris recognition system reached 97.14% and 98.28% respectively, and the running time was significantly reduced, up to 0.699 s and 0.758 s respectively.