虹膜图像的特征分析研究
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摘要
伴随着信息技术的迅猛发展,人们对信息安全的要求也相应的提高。传统的身份识别方法由于其自身固有的缺点已经不能满足社会发展的要求,生物特征识别技术应运而生。
作为生物特征识别技术的一个重要组成部分,虹膜识别有着非常高的准确性和可靠性。同其他生物特征相比,虹膜具有很多可区分的信息。通过分析研究虹膜上丰富的纹理特征,结合不同的特征提取方法和分类器可以实现虹膜的身份认证。
虹膜作为人体上的一个重要器官,不仅在身份识别中具有重要的作用,在医学诊断方面,虹膜也具有一席之地。在西方,虹膜诊断学(Iridology)已经经历了一百多年的发展,逐渐形成了一套完整的体系。虹膜诊断是利用个体虹膜图像中发生变异的细节结构,如虹膜中出现的坑洞、裂缝、斑块、线条、颜色变化,通过判断虹膜的这些异常达到疾病诊断的目的。
基于虹膜身份识别和虹膜诊断的不同应用领域,本文从虹膜图像的纹理和结构两方面入手,对虹膜图像的特征进行了详细的分析。主要研究内容包括以下几个方面:
1.针对传统虹膜定位方法速度慢,需要参数累加计算等缺点,提出了结合虹膜灰度特点的最长弦检测的快速虹膜定位方法。它利用了虹膜瞳孔的生理特征,在二值化后的虹膜图像中寻找水平最长弦,从而定位瞳孔的大致位置,在此基础上经过进一步的细化提取准确的瞳孔中心位置。然后结合微积分算子完成虹膜外边缘的检测。极坐标转换的虹膜归一化方法,消除了图像旋转平移的干扰。计算过程分析与实验结果都表明该算法计算次数大大减少,从而缩短了计算时间。在保证定位成功的前提下,定位精度也达到了亚像素级水平。
2.研究了利用虹膜纹理的分形维数对虹膜图像进行自动分类的方法。首先定义并研究了虹膜图像的纹理复杂度,然后利用计盒维数法计算虹膜图像的分形维数,得到上下两组分形维数值。最后利用双阈值算法和BP神经网络对虹膜进行分类。根据虹膜纹理的复杂度,将虹膜分成了四类。考虑到边界效应的存在,我们在原有的分类基础上增加了三条补充原则,使双阈值算法分类准确率最终提高到98.28%。
3.研究了基于关键点之间相对距离的虹膜识别新算法。结合虹膜图像
With the rapid development of information technology in society, personal authentication is becoming necessary in more and more fields. The traditional personal authentication methods cannot keep up with the requirements of the society because of their inherent defects. Under such circumstance, biometrics emerged as the time requires.
As an important composing of biometrics, iris recognition is the most stable and reliable biometric technology. Compared with other biometric features, iris contains more distinctive information. Through analyzing the iris feature and combining with different feature extraction methods, we can realize iris recognition.
As an organ of human body, iris plays an important role not only in identity identification, but also in medical diagnosis. In the west, iridology has a history more than one hundred years, and it has become a more completed system. Iridology is the study of the patterns and markings in the iris of the eye. Through analyzing the changes of the texture and the color of the iris, such as the crypt, the lacunae, the spots and lines, person can know if he is healthy.
Based on the different applications of iris recognition and iris diagnosis, this paper studies the iris feature from textural pattern and structural pattern. The main research includes following parts:
1. Owing to the shortcoming of the parameter accumulation operation and the slow operation speed of traditional iris localization algorithms, this dissertation investigates a fast algorithm for iris localization based on the detection of the longest chord. The proposed method accurately and fleetly locates the inner boundary of the iris by means of detecting the diameter of the pupil in the binary image. Then, the outer boundary is detected by a deformable circular template. The iris normalization is invariant for translation, rotation and scale after mapping into polar coordinates. Experimental results show this method has less computation and faster speed compared with other methods, the precision has achieved the pixels level and obtained the higher localization success rate.
作为生物特征识别技术的一个重要组成部分,虹膜识别有着非常高的准确性和可靠性。同其他生物特征相比,虹膜具有很多可区分的信息。通过分析研究虹膜上丰富的纹理特征,结合不同的特征提取方法和分类器可以实现虹膜的身份认证。
虹膜作为人体上的一个重要器官,不仅在身份识别中具有重要的作用,在医学诊断方面,虹膜也具有一席之地。在西方,虹膜诊断学(Iridology)已经经历了一百多年的发展,逐渐形成了一套完整的体系。虹膜诊断是利用个体虹膜图像中发生变异的细节结构,如虹膜中出现的坑洞、裂缝、斑块、线条、颜色变化,通过判断虹膜的这些异常达到疾病诊断的目的。
基于虹膜身份识别和虹膜诊断的不同应用领域,本文从虹膜图像的纹理和结构两方面入手,对虹膜图像的特征进行了详细的分析。主要研究内容包括以下几个方面:
1.针对传统虹膜定位方法速度慢,需要参数累加计算等缺点,提出了结合虹膜灰度特点的最长弦检测的快速虹膜定位方法。它利用了虹膜瞳孔的生理特征,在二值化后的虹膜图像中寻找水平最长弦,从而定位瞳孔的大致位置,在此基础上经过进一步的细化提取准确的瞳孔中心位置。然后结合微积分算子完成虹膜外边缘的检测。极坐标转换的虹膜归一化方法,消除了图像旋转平移的干扰。计算过程分析与实验结果都表明该算法计算次数大大减少,从而缩短了计算时间。在保证定位成功的前提下,定位精度也达到了亚像素级水平。
2.研究了利用虹膜纹理的分形维数对虹膜图像进行自动分类的方法。首先定义并研究了虹膜图像的纹理复杂度,然后利用计盒维数法计算虹膜图像的分形维数,得到上下两组分形维数值。最后利用双阈值算法和BP神经网络对虹膜进行分类。根据虹膜纹理的复杂度,将虹膜分成了四类。考虑到边界效应的存在,我们在原有的分类基础上增加了三条补充原则,使双阈值算法分类准确率最终提高到98.28%。
3.研究了基于关键点之间相对距离的虹膜识别新算法。结合虹膜图像
With the rapid development of information technology in society, personal authentication is becoming necessary in more and more fields. The traditional personal authentication methods cannot keep up with the requirements of the society because of their inherent defects. Under such circumstance, biometrics emerged as the time requires.
As an important composing of biometrics, iris recognition is the most stable and reliable biometric technology. Compared with other biometric features, iris contains more distinctive information. Through analyzing the iris feature and combining with different feature extraction methods, we can realize iris recognition.
As an organ of human body, iris plays an important role not only in identity identification, but also in medical diagnosis. In the west, iridology has a history more than one hundred years, and it has become a more completed system. Iridology is the study of the patterns and markings in the iris of the eye. Through analyzing the changes of the texture and the color of the iris, such as the crypt, the lacunae, the spots and lines, person can know if he is healthy.
Based on the different applications of iris recognition and iris diagnosis, this paper studies the iris feature from textural pattern and structural pattern. The main research includes following parts:
1. Owing to the shortcoming of the parameter accumulation operation and the slow operation speed of traditional iris localization algorithms, this dissertation investigates a fast algorithm for iris localization based on the detection of the longest chord. The proposed method accurately and fleetly locates the inner boundary of the iris by means of detecting the diameter of the pupil in the binary image. Then, the outer boundary is detected by a deformable circular template. The iris normalization is invariant for translation, rotation and scale after mapping into polar coordinates. Experimental results show this method has less computation and faster speed compared with other methods, the precision has achieved the pixels level and obtained the higher localization success rate.
引文
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