活体虹膜图像的定位与分割

介绍了一种活体虹膜的定位与分割算法。算法主要分为两部分：圆环的定位与非虹膜区域的去除。本算法根据眼睛的生理特点和数字虹膜图像的实际情况,利用传统定位方法与数学形态学相结合对虹膜区域进行快速而准确的定位,并分别提出了去除眼睑、睫毛和光斑影响的解决方案。算法中也考虑到实际应用可能遇到的影响虹膜定位与分割的问题。实验表明,该算法取得较好的分割结果,并且具有鲁棒性。     

High-Performance Iris Recognition for Mobile Platforms

In spite of a fact that many standalone iris recognition solutions are successfully implemented and deployed around the world, development of a reliable iris recognition solution capable to provide high recognition performance (both in biometric quality and speed) on mobile device is still an actual task. Main issues related to iris recognition in the mobile devices consist in uncontrollable capturing conditions and limitations in computation power. The aim of the proposed approach is to eliminate aforementioned issues by providing user with comprehensive feedback and, at the same time, performing the most computationally complex operations only on the images of the best quality. Key features of the proposed approach are multi-stage algorithm structure, novel iris image quality estimation and adaptive iris feature vector quantization algorithms. These features allow to achieve high recognition accuracy and real-time performance which are proved by experimental results.     

用于虹膜识别的轮廓波特征提取

为获得高品质的虹膜纹理特征,针对小波变换方向选择性差的局限和虹膜图像纹理丰富的特点,本文提出了一种基于轮廓波(Contourlet)变换的虹膜特征提取方法.首先对预处理后的虹膜图像进行Contourlet分解,然后根据高低频子带所表征的信息,采用不同特征提取策略,提取其低频分量的均值及标准差和不同尺度、不同方向上高频子...     

一种基于结构特征的虹膜识别方法

提出了一种基于特征点比对的虹膜识别方法。与现有利用纹理分析进行识别的方法相比,这一方法具有编码效率高,抗噪声能力强,识别准确性好等特点。该方法首先利用LOG(LaplacianofGaussian)滤波器提取特征区域,然后在特征区中确定特征点。在方法中提出了基于特征点的相似性度量方法和弹性特征点比对方法。试验结果表明:这种方法分类效果较好。     

虹膜识别算法的应用研究

研究虹膜识别问题.针对传统识别算法不能够很好的消除平移,缩放和旋转对于虹膜识别的影响,以及一维 log Gabor滤波特征提取方法丢失二维信息的缺陷,导致虹膜识别的正确率低的难题,提出一种改进的虹膜识别算法.首先在虹膜预处理过程中采用粗定位和精定位相结合的虹膜定位方法获取虹膜内外边缘,并用直方图均衡化来增强归一化的虹膜图像.突出虹膜纹理,从而降低光照不均匀性的影响,然后二维 logGabor 滤波取代一维 log Gabor 滤波来提取虹膜纹理特征,克服一维 log Gabor 滤波丢失二维信息的缺陷,最后对提取的特征进行编码,采用欧式距离进行匹配得到识别结果.通过 CASLA虹膜数据库进行了仿真,实验结果表明,相对于当前典型的虹膜识别方法,提高了虹膜识别的正确率,识别速度相应加快,在虹膜识别领域中有着广阔的应用前景.     

Developing Iris Recognition System for Smartphone Security

Smartphones have become an important way to store sensitive information; therefore, users’ privacy needs to be highly protected. This can be done by using the most reliable and accurate biometric identification system available today: iris recognition. This paper develops and tests an iris recognition system for smartphones. The system uses eye images that rely on visible wavelength; these images are acquired by the smartphone built-in camera. The development of the system passes through four main phases: the first phase is the iris segmentation phase, which is done in three steps to detect the iris region from the captured image, which contains the eye and part of the face using Haar Cascade Classifier training, pupil localization, and iris localization using a Circular Hough Transform. In the second phase, the system applies normalization using a Rubber Sheet model, which converts the iris image to a fixed size pattern. In the third phase, unique features are extracted from that pattern using a Deep Sparse Filtering algorithm. Finally, in the matching phase, seven different matching techniques are investigated to decide the most appropriate one the system will use to verify the user. Two types of testing are conducted: Offline and Online tests. The BIPLab database and a collected dataset are used to measure the accuracy of the system phases and to calculate the Equal Error Rate (EER) for the whole system. The average EER is 0.18 for the BIPLab database and 0.26 for the collected dataset.     

虹膜识别中标准化区域分辨率的研究

针对虹膜区域图像大小不同,难以实现虹膜的比较问题,提出了根据虹膜最小内边界周长确定角度分辨率和根据虹膜最小内、外边界半径差确定径向分辨率的方法,将不同分辨的虹膜图像转换为相同分辨率的矩形区域。结果表明,采用该方法得到的标准化维数对于虹膜的识别是有效的。     

虹膜识别系统的研究

介绍了基于人眼虹膜的一种生物识别技术,描述了虹膜识别系统的组成结构与实现,给出每部分组成模块,并详细讨论了虹膜识别算法。     

生物特征识别技术之虹膜识别

人眼睛的外观图由巩膜、虹膜、瞳孔三部分构成。巩膜即眼球外围的白色部分,约占总面积的30%;眼睛中心为瞳孔部分,约占5%;虹膜位于巩膜和瞳孔之间,包含了最丰富的纹理信息,占据65%。外观上看,虹膜由许多腺窝、皱褶、色素斑等构成(见图1),是人体中最独特的结构之一。虹膜的形成由遗传基因决定,人体基因表达决定了虹膜的形态、生理、颜色和总的外观。人发育到八个月左右,虹膜就基本上发育到了足够尺寸,进入了相对稳定的时期。除非极少见的反常状况、身体或精神上大的创伤才可能造成虹膜外观上     

用于虹膜识别的眼睑及眼睫毛遮挡检测

分别针对眼睑和眼睫毛遮挡噪声,利用其灰度和形状信息提出了2种灰度形态学检测算法.1)设计弧线形的形态学结构元素,经过灰度开启运算、图像分割和边缘检测,获得眼睑边缘的候选点集,再利用Bézier曲线拟合出眼睑边缘;2)构造交叉形的形态学结构元素,通过灰度开启运算得到直方图具有分段特性的虹膜图像,经二值化检测出眼睫毛.实验结果表明:文中算法能有效地检测2种遮挡噪声,有助于降低虹膜识别系统的等错误率,提高模式的可分性.     

FIRME: Face and Iris Recognition for Mobile Engagement

Mobile devices, namely phones and tablets, have long gone “smart”. Their growing use is both a cause and an effect of their technological advancement. Among the others, their increasing ability to store and exchange sensitive information, has caused interest in exploiting their vulnerabilities, and the opposite need to protect users and their data through secure protocols for access and identification on mobile platforms. Face and iris recognition are especially attractive, since they are sufficiently reliable, and just require the webcam normally equipping the involved devices. On the contrary, the alternative use of fingerprints requires a dedicated sensor. Moreover, some kinds of biometrics lend themselves to uses that go beyond security. Ambient intelligence services bound to the recognition of a user, as well as social applications, such as automatic photo tagging on social networks, can especially exploit face recognition. This paper describes FIRME (Face and Iris Recognition for Mobile Engagement) as a biometric application based on a multimodal recognition of face and iris, which is designed to be embedded in mobile devices. Both design and implementation of FIRME rely on a modular architecture, whose workflow includes separate and replaceable packages. The starting one handles image acquisition. From this point, different branches perform detection, segmentation, feature extraction, and matching for face and iris separately. As for face, an antispoofing step is also performed after segmentation. Finally, results from the two branches are fused. In order to address also security-critical applications, FIRME can perform continuous reidentification and best sample selection. To further address the possible limited resources of mobile devices, all algorithms are optimized to be low-demanding and computation-light.     

基于卷缩轮编码的虹膜识别

摘要利用图像纹理元的灰度模式及统计特征完成虹膜图像的卷缩轮分割,并在卷缩轮分割的基础上实现对虹膜的识别;实验结果表明,该方法具有很好的识别效果。     

支持向量机虹膜识别算法研究

提出一种基于极值加权平均分数维特征提取和支持向量机分类器识别的虹膜识别方法.利用形态学和圆形边缘检测算子定位虹膜,并将虹膜纹理映射到极坐标空间;定义了一种新的图像分敷维--极值加权平均分数维,用于提取虹膜特征;利用支持向量机分类器对虹膜特征矩阵进行匹配识别.试验表明,基于极值加权平均分数维特征提取和支持向量机分类嚣识别的虹膜识别系统识别率高,速度快.     

Iris Recognition: The Path Forward

Iris recognition systems have made tremendous inroads over the past decade, but work remains to improve their accuracy in environments characterized by unfavorable lighting, large stand-off distances, and moving subjects.     

基于改进的Log-Gabor小波的虹膜识别算法

对二维Gabor复小波应用于虹膜识别进行了详细的理论分析,提出了采用二维奇对称Gabor小波代替二维Gabor复小波来提取虹膜纹理特征的改进算法.在此基础上,进一步提出一种采用改进的二维 Log-Gabor小波来提取虹膜纹理特征,采用汉明距来进行特征匹配的新方法,克服了二维Gabor复小波应用于虹膜识别的缺陷.与已有算法进行比较的实验数据表明,采用二维奇对称Gabor小波的改进算法在识别率略有提高的基础上,能大大地减少编码存储空间以及编码和匹配的时间.而采用改进的二维Log-Gabor小波算法则进一步的提高了识别率.     

一种快速而准确的虹膜识别方法

提出了一种快速而准确的虹膜识别算法,基本思想是:通过图像预处理,确定虹膜的位置和大小,并将直角坐标系下的环形虹膜展开成极坐标系下的矩形虹膜;利用寻找局部灰度极小值点的方法寻找图像中每条扫描线上的特征点,去掉伪特征点以后,得到固定长度的虹膜编码;计算两个虹膜编码之间的海明距离,根据海明距离给出识别结果.     

基于卷缩轮信息的虹膜识别

本文利用图像纹理元的灰度模式及统计特征完成卷缩轮的分割,并在卷缩轮分割的基础上实现对虹膜的识别;同时,运用该方法进行了实验,实验效果很好。     

用于身份识别的虹膜图像预处理方法研究*

跟踪研究了虹膜图像的预处理方法;针对现有算法的局限性作了改进,提出了基于小波变换的虹膜图像消噪的方法,并针对虹膜图像的特点,提出了新的虹膜定位检测算子,使虹膜定位更加迅速、精确;最后进行了虹膜图像的增强和校准处理.实验结果表明,该方法能精确地检测到虹膜,提高了虹膜识别的速度和精确性,并有良好的稳健性.     

基于MATLAB GUI 的虹膜识别算法测试平台

虹膜识别是一种重要的利用生物学特征进行的身份识别技术,针对虹膜识别人们已经提出了许多算法,但如何比较、评价各种算法,还没有一个统一的标准和测试平台,为此该文建立了一个虹膜识别算法的MATLAB图形用户界面（GUI）,给出了建立虹膜数据库应该考虑的问题。在该平台下,可以对虹膜识别的有关问题进行研究。对同一个虹膜数据库进行实验,可以获得不同算法的性能指标比较。该平台除了提供统一的操作界面进行各种算法比较研究外,还可以允许用户对新算法进行实验研究。结果表明,该平台能够实现各种算法的集中管理,并输出可视实验结果,很方便进行虹膜识别算法的研究和比较。     

基于曲面匹配的虹膜识别方法

提出了一种基于曲面匹配的虹膜识别方法,直接利用2幅虹膜图像对应的像素点灰度进行匹配。该方法主要包括虹膜图像尺寸和灰度范围的归一化、匹配区域的选择、匹配相似度阈值的估计以及对人眼旋转的自动校正。该文选择在眼毛、眼睑通常遮挡不到的一个较小的区域作为虹膜匹配区域,使该方法具有较宽的适应能力。没有特征提取过程,使得该方法具有计算量小的特点。图像之间的匹配过程使得伪造和窃取虹膜编码变得困难。实验表明该方法具有较高的识别率。