CN1160446A - 自动的无侵害的虹膜识别系统和方法 - Google Patents
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Abstract
一种允许用户自己将他或她的眼球(216)放置入一个成像器(200)的视野内而不需任何物理接触的虹膜识别系统和方法,它在空间内在定义用户眼球的数字化视频图像的数据中确定只定义它的虹膜的那部分数据,以及应用归一化空间相关运算以便首先将在空间内互相对准的用户眼球虹膜和模型虹膜的空间特性进行比较从而确定匹配优度值以及然后根据匹配优度值的一定组合判定用户虹膜是否与模型虹膜相匹配。
Description
按照政府合同美国政府对本发明具有权利。
现有技术包括用于根据对人们内眼或外眼的具体特征的查验从而对个别人员进行独特标识的各种技术。现有技术还包括一种用于在眼球跟踪图像检测设备中将噪音自特征部分中分离出来的技术,这在1991年5月14日授权于Tomono等人的美国专利5,016,282中已公开。这些现有技术中的一项涉及对至少一个人眼球的虹膜外部的具体特征的视觉查验。在这方面的参考材料有1987年2月3日授权于Flom等人的美国专利4,641,349,1994年3月1日授权于Daugman的美国专利5,291,560,及1993年11月在IEEE“模式分析与机器智能”论文集第15卷第11期第1148-1161页上的Daugman的文章“使用统计学独立测试的高可靠性人的视觉识别”。如上述专利和文章所叙述的,人们虹膜的可见纹理可用于很准确地将一个人与另一人区别开。因此,作为例子,虹膜识别可用于以下目的:对安全设施的存取或对自动取款机(ATM)自动付款的控制。虹膜识别系统使用一个成像器将每个试图存取者的虹膜视频成像,另外使用计算机视觉图像处理装置将此虹膜视频图像与数据库中文件内的参考虹膜图像相比较。例如,试图存取者可先输入一个个人标识码(PIN),从而允许将该人的虹膜视频图像与文件内他或她的参考虹膜图像关连起来。此外,虹膜识别系统在例如医学上检查眼球外部这样的医学诊断上是有用的。
从实用观点看,现有技术虹膜识别系统和方法有下述问题。
首先,以前用于获取眼球虹膜的高质量图像的方案具有:(i)一个用于将测试对象靠近已知标准配置的侵害性定位装置(例如头靠或咬合杆);(ii)一个用于提供标准化眼球照明的受控光源;及(iii)一个用于获取所定位和照明的眼球图像的成像器。这种标准设备具有一系列限制,包括:(a)用户发现定位所需物理接触令人不快,及(b)以前方法中用于获取高质量、高对比度图像所需照明亮度令用户厌烦。
第二,以前用于在眼球图像中将虹膜定位的方案采用了虹膜的参数化模型。这些模型的参数逐步迭代地适配于眼球图像并得到增强以提高对应于虹膜边界的区域亮度。模型的复杂性包括自用于划分虹膜的内、外边界的同心圆至考虑部分地闭合眼睑的影响的更精细模型。用于增强虹膜边界的方法包括基于梯度的边缘检测以至形态学滤波。这些方法的主要限制在于需要用作迭代适配过程起点的良好初始条件以至庞大的计算费用。
第三,以前用于将自试图存取者眼球视频图像中获取的定位的虹膜数据图像与数据库中文件内一个或更多个参考的定位的虹膜数据图像进行模式匹配的方案能提供这些虹膜数据图像间合理的鉴别,但却需要庞大的计算费用。
本发明提出的改进的系统和方法能对现有技术虹膜识别系统和方法的上述三种方案中的一种或更多种方案中的缺点提出解决方法。
对上述三种方案的第一种方案的解决方法包括一个可由较大第一边框和较小第二边框实现的无侵害对准机构,此两个边框具有相似的几何形状、基本上以成像器透镜为中心并与该透镜有不同距离以允许用户自己将他或她的眼球放入成像器视野内而不必使他或她的眼球与系统有任何物理接触,由于投影原理,较小边框基本上都遮盖了较大边框。
对上述三种方案中第二种方案的解决方法包括以下步骤:将个人眼球虹膜的边缘边界、所述虹膜的瞳孔边界、和所述眼球的上、下眼睑的边界中至少一个边界进行图像滤波以获取一个增强图像从而将数字数据划定为只用于定义个人眼球虹膜的个人眼球数字化图像部分,然后使用一个包含表决机制的装置将增强图像进行曲线变换。此种复原虹膜边界的方案不必知道任何初始条件而只需个人眼球的数字数据表示。
对上述三种方案中第三种方案的解决方法包括一个为安全存取控制提供自动虹膜识别的模式匹配技术。该模式匹配技术对只定义某个试图存取者的眼球虹膜的数字化图像的第一数字数据作出响应,同时对以前存储的只定义特定个人眼球虹膜的数字化图像的第二数字数据作出响应,并在众多空间尺度的每一个尺度处采用归一化空间相关技术将在空间互相对准的某个个人与特定个人的相应虹膜的每一项明显的空间特性进行比较以便定量地确定在众多空间尺度中每一尺度处的匹配优度值。只代表某个个人的所述眼球虹膜的数字数据是否与只代表特定个人的所述眼球虹膜的数字数据相匹配需根据在众多空间尺度的每一尺度处所定量地确定的匹配优度值的一定组合而确定。
结合附图阅读下列详细说明,可容易地理解本发明宗旨,附图中有:
图1是包含本发明原理的一个自动无侵害虹膜识别系统的功能框图;
图2阐述包含本发明原理的虹膜检测装置的一个实施例;
图2a与2b一起阐述用于增强图2实施例的虹膜检测装置改型;以及
图3阐述本发明所用对一个输入的虹膜图像自动处理以提供完整虹膜定位的计算步骤流程。
图1中一个自动无侵害虹膜识别系统包括一个用于获取一个人员(作为一定的准备由该系统识别的预定人员,此后称为“用户”)的输入的虹膜图像,通常为视频图像的虹膜检测装置100(更详细地示于图2中);用于自动地处理输入的虹膜图像(使用图3中所示计算步骤)以提供自检测装置100向其送来的输入的虹膜图像的完整定位的虹膜定位装置102;及用于自动地将自装置102送来的定位的虹膜信息模式与一定预定人员的存储的模型虹膜106的模式相比较并用于以高准确度判定该用户是否为该一定的预定人员的模式匹配装置104。
如图2所示,检测装置100包括例如一个视频摄像机那样的成像器200,一个光源阵列202,漫射器204,圆形偏光镜206,较大方形边框208,较小方形边框210和图像帧获取器212。
成像器200通常是一个低亮度级视频摄象机,例如硅倍加靶(SIT)摄像机,它所具有的光学部件包括一个伸过漫射器204中心因而不干扰成像器获取清晰图像的操作的远距摄像/宏透镜214。透镜214可获取位于透镜214前方远处的用户眼球216的高分辨率图像,因而不需眼球216特别靠近成像器200。
自围绕成像器200的光源阵列202射出的光通过漫射器204和偏光镜206将位于偏光镜206前的用户眼球216照明。漫射器204是一个用作第一滤光器的散射屏,它用于两目的:向眼球216提供均匀照明及在宽广范围内将眼球216处的辐射能积聚,从而使相当数量的光强分布在用户视野内,如果相同能量集中在单点光源上,则将使用户厌烦。位于透镜214前面的偏光镜206用作第二滤光器,它改善角膜处镜面反射的效果,不然眼球216的下层组织将被扰乱。更具体地说,自偏光镜206发射的光具有特定旋转方向。当此光射至反射镜面(例如角膜)上时,被反射的光仍被偏振,但却在相反方向上。此反方向光不会返回通过偏光镜206,因而被阻挡而不为成像器200所感测。然而,眼球的将入射光漫散地反射的部分(如虹膜)将入射光散射,因而此光将返回通过偏光镜206并随后用于形成图像。应注意到,严格地讲,使用线性偏振后再使用四分之一波延迟器即完成圆形偏振;因此它只调谐于特定波长范围。
如图2中所示,较大和较小方形边框208和210两者都在位置上与透镜214轴同心,其中较大方形边框208位于偏光镜206前较短距离处和较小方形边框210位于偏光镜206前较长距离处。这些边框208和210作为对准机构用于让用户自己将他或她的眼球定位于成像器200的视野内而不必使眼睛与系统有物理接触。定位的目的是约束被成像物体(例如眼球216)的三个平行移动自由度,以使它位于成像器200的感测器阵列(未示出)的中心线上并在透镜214的聚焦平面距离处。简单的投影几何学向用户提供提示以使他或她自己设法在空间找到满足这些条件的位置。具体地说,如虚线220所示只有一个眼球216的空间位置,即在该处较小方形边框210的方形轮廓线才能完全地遮住较大方形边框208的方形轮廓线。此空间位置在偏光镜206前的距离事实上比较小方形边框208的距离更长。方形边框208和210的相对尺寸和距离如此选择以便在将眼球恰当地定位时使它们的方形轮廓线重叠,而当较小和较大方形边框208和210没有对准时,可以向用户提供有关他或她眼球当前位置对准的准确度的连续反馈信息。此对准过程可称为与人类油标敏锐度相像的油标对准,使用超精确度对准细线和其它微小靶物的能力。
此外,虽然图2实施例的较大和较小边框208和210两者都具有方形轮廓线形状,但应该理解这些较大和较小边框的轮廓线条可具有除方形以外的类似的几何形状,以便当眼球恰当地定位时它们的类似的几何线条重叠起来,同时当较小和较大边框没有对准时,可以向用户提供有关他或她眼球当前位置的对准准确度的连续反馈信息。
在任何情况下,接收用户眼球216的精确地聚焦的光强度图像(具有可予忽略的镜面反射噪音)的成像器200获取此眼球图像的连续视频帧。帧获取器212(它是一个标准数字帧获取器)将由视频帧中选出的一帧所定义的眼球图像存储起来。来自帧获取器212的这个所存储眼球图像被向前送至装置102(示于图2中)供虹膜定位之用。
为了阐述目的,假定用户或者试图存取一个安全设施,或者试图存取一个ATM。在任何一种情况下,在用户首先依靠方形边框208和210以上面所述方式将他或她自己眼球216放入成像器200的视野内而不必与系统有任何物理接触之后,用户接着可以按动一个按钮(未示出)以使帧获取器212将由从成像器200获取的当前出现的视频帧所定义的眼球图像存储起来。因此,用户按动按钮的操作类似于用户按动静止照相机快门以将一帧风景摄录在静止照相机的底片上的操作。
图2中所示和以上描述的结构组成检测装置100的一个基本实施例。然而,为了使用方便和有可能获取图像,考虑到各用户的身材和面部特征彼此都不相同,希望增强检测装置100的结构以使由成像器看到的和由帧获取器存储的任何用户眼球的图像位置与用户的具体身材和面部特征无关。此外,在控制对安全设施的存取中,希望在用户将他或她自己的眼球放入成像器视野内的地点附近区域内提供一个视频摄像机监视,同时提供可用于标识一个试图存取的用户的附加视觉信息。图2a和2b一起阐述了提供这种增强的装置100的结构的改型。
如图2a所示,检测装置100的结构改型包括具有相对地宽广视野、用于获取至少试图存取的用户226的头像的图像224的低分辨率成像器222。该改型还包括具有相对地狭窄视野、由操纵镜230的位置所控制的、用于获取用户226的眼球图像232的高分辨率成像器228(其中成像器228对应于图2的成像器200)。下面描述的图2b中所示类型的图像处理装置使用成像器222的连续视频帧中包含的信息并根据美国专利4,692,806、5,063,603和5,067,014中的一项或更多项中公开的现有技术宗旨对操纵镜230的位置进行控制调整,这些专利都在这里引用为参考资料。
更具体地说,检测装置100的改型涉及对人们头、脸和眼的现场图像检测和跟踪以识别操作员头部(以及其面部特征,例如眼和虹膜)的初始位置并随后进行跟踪。使用由成像器222获得的图像信息的改型方案将工作分为三部分。第一部分与头部及其各部分特征的粗略定位和跟踪有关。第二部分使用粗略定位和跟踪信息进行变焦放大处理并对眼睛部分尤其是虹膜在位置上和时间上的估值进行精细处理。第三部分与动作跟踪有关。
眼睛定位的第一部分是一个用于向系统提示存在着可能的用户和用于选择用户的可能位置的机构。这种提示机构是图2b中所示能量变换锥体(下面将更详细地讨论),其中每隔一个时间间隔求记录的图像差值和求平方值。使用求差值和平方的图像上的高斯锥体产生不同分辨率下的能量变换。在粗略分辨率下对变换进行分析,如发生变换则向系统提示一个可能的用户已进行成像器视野。其它提示机构包括立体摄像,将自两个位置录下的两个图像的差异进行计算以检测用户的接近程度并向系统提示有物体接近。
眼球定位的第二部分是一个用于将用户头部和眼部初始定位的机构。定位是使用一个模式树完成的,该模式树包括一个普通用户的模型,例如粗略分辨率下的头部样板及眼、鼻和嘴的样板。该提示机构提供一些供图像与模型匹配的样板匹配过程用的可选位置。初始匹配用于在粗略分辨率下确定例如头部那样的粗略特征,然后使用来自粗略分辨率匹配操作的信息确定例如眼、鼻和嘴那样的精细分辨率特征。
眼球定位的第三部分用于一旦看见头和眼后即跟踪它们。这由一个动作跟踪器完成,它在先前图像帧与当前帧之间完成相关匹配。相关匹配使用眼球定位中所用特征,但也可使用其它特征,如头发,这对短期内跟踪有用,同时因人而异。
上述三个部分的结果是在来自成像器222的图像224中提供眼球定位,以及如使用立体摄像,则提供眼球的近似距离。操纵镜230利用此信息将成像器228对准眼球以获取图像。已知眼球在图像224中的位置,它的近似距离及成像器222和成像器228之间的已知几何关系后,可以容易地算出使用成像器228获取眼球的对准方向。如不知道眼球距离,则成像器228指向对应于近似的预计距离的位置,自此位置出发它指向对应于围绕预计距离附近的距离的位置。如成像器228和成像器222配置为在光学上对准的,则仅图像224中眼球图像位置即足够用于对准成像器228。一旦成像器228初始地对准眼球,来自成器228的图像即用于将眼球保持于视野内。这用于补偿眼球跳动和用户的正常移动。这类移动在来自成像器222的图像如图像224中是不显著的,但在来自成像器228的图像如图像232中却是明显的。跟踪步骤与已描述的用于跟踪头部和眼部的步骤相同,所不同的是用户眼球图像,如图像232中所用特征为眼球瞳孔、眼睑边缘边界和眼睑纹理。
参照图2b,其中显示图像处理器的功能框图,该处理器对来自成像器222的图像作出响应,控制操纵镜230位置以使用户226眼球图像232位于成像器228视野内。
具体地说,由成像器222输出的代表图像224的连续帧的视频信号在数字化后作为输入量G0送至高斯锥体234。经恰当延迟后,输入量G0向前送至高斯锥体输出端以在与图像224相同的分辨率和采样密度下提供图像锥体的G0图像236。此外如锥体技术中所已知的,高斯锥体234包括级联卷积和二次采样阶段,用于求出作为高斯锥体234输出量的图像锥体的减低分辨率G1输出图像238和G2输出图像240。
高斯锥体234的相应输出量G0、G1和G2由帧延迟器242延迟一个或更多个帧周期。减法器244将G0、G1和G2中每一帧的当前帧和延迟帧的相应像素偏振幅值间之差作为它的输出量提供出来,因而使静止图像体幅值相对于移动图像体幅值为最小。将减法器244输出量乘方(如块246所标示)以放大此极小化值并消除极性以提供G0、G1和G2能量变换锥体(如相应块248、250和252所标示)。在一个技术中已知的由粗及精的过程中,该能量变换锥体信息接着可用于控制图2a中操纵镜230的位置。
此外,该改型可使用样板匹配法,如在上述美国专利5,063,603中提出的用于物体识别的方法。另一方案是如在上述美国专利4,692,806中所公开的,不同样板匹配而用基于特征算法实行粗略定位和跟踪以提供类似信息。
此外,该改型可用机会主义方式获取-序列图像直至获得一帧其质量适合于随后操作的图像时止。另一方案是可从这一序列帧中获取感兴趣的区域片断并随后将它们拼凑起来以产生一帧具有合适质量的单幅图像。另外,这些改型方案中任何一个都可用于变焦放大处理和获取眼球和虹膜以外的面部特征的高分辨率图像。例如,可用类似方式获取操作人员唇部的高分辨率图像。
不论具有或不具有图2a和2b的改型所提供的增强,图2中所示系统都可用不同方式加以推广。首先,该系统可在可见光以外的频谱中工作(例如近红外)。因此这里所用“光”一词既包括可见的也包括不可见频谱中的光辐射。为做到这点,发光剂的频谱分布以及四分之一波延迟器的波长调谐应与所需频带相匹配。第二,该系统可使用一个标准视频摄像机以代替低亮度级摄像机,当然需要应用更高亮度的发光剂。第三,可选择其它透镜系统,包括使用一个自动聚焦变焦透镜。这一附加变化将使准确度较用户使用油标对准过程的系统差。第四,也可使用油标对准过程的其它具体事例。例如,可投射一对光束以使处于正确位置的用户看到单个光点而处于其它位置时看到两个光点。第五,该系统可与一个有源跟踪成像器和相关连的软件一起使用(如以上结合图2a和2b所描述的)以便自动地定位和跟踪用户眼球,从而替代(或补充)无源油标对准机构。这种推广不足之处是需要用户配合。
作为输入量送至定位装置102的检测装置100输出量中的数字形式数据定义了对应于帧获取器212中存储的特定视频帧的相对地高分辨率眼球图像。图3图解地显示由定位装置102对向其输入的眼球图像数据实行的一序列连续的数据处理步骤。
更具体地说,输入图像300代表自检测测装置100输入至定位装置102的相对地高分辨率眼球图像数据。第一数据处理步骤302将输入图像300求平均并缩小。这通过使用低通高斯滤波器将定义输入图像300的数据实行卷积运算而完成,该高斯滤波器用于在空间求平均值因而减小高频噪音。由于空间求平均值在空间域内引入冗余度,所以被滤波的图像接着被二次采样而不更多地损失任何信息。该二次采样的图像用作随后处理的基础,其优点是其较小尺寸和较低分辨率所需计算量较初始的完全尺寸的输入图像300少。
将虹膜定位的下一个数据处理步骤包括将虹膜边界的不同部分顺序定位。按顺序讲,步骤304将虹膜边缘边界(或外边界)定位,步骤306将虹膜瞳孔边界(或内边界)定位,及步骤308将眼睑边界(它可能遮盖住一部分虹膜)定位。选择此顺序的依据是所涉及的图像特征的凸显性以及所定位部分对附加定位部分的约束能力。每一部分的定位步骤用两个子步骤完成。第一子步骤包括调整到高反差度图像位置的预计图形的边缘检测操作。此调整的依据是感兴趣部分的边界的一般特性(如定向)及由先前所分离出来的边界部分所提供的具体约束条件。第二子步骤所包括的方案对所检测的边缘像素表决以便为感兴趣边界部分的参数化模型举出具体数值事例。最简单地说,此步骤可认为是参考资料中包括的美国专利3,069,654所公开的一般化的Hough变换。
更详细地说,对于步骤304的虹膜边缘边界讲,一个基于梯度的边缘检测器将图像滤波,该检测器调谐的方向有利于近垂直方向的操作。此方向选择性以下列事实为依据:即使在眼睑闭合的面部上,虹膜边缘的左端和右端部分应能清楚地看出,其方向接近垂直(这假定头部处于竖直位置)。该虹膜边缘边界的模型是一个参数化的圆,其圆心的两个坐标是XC和YC和半径为r。所检测的边缘像素变稀少并根据给定图像位置(X,Y)的允许值(XC,YC,r)被变换入一个三维空间(XC,YC,r)。具有最多表决票的(XC,YC,r)点用于代表虹膜边缘边界。对此边界所加唯一附加约束条件是它位于给定眼球图像内。
更详细地说,对于步骤306中的瞳孔边界,使用并非定向地调谐的基于梯度的边缘检测器将图像滤波。类似于虹膜边缘边界,瞳孔边界模型是一个圆。同样地,边缘像素变稀少并被变换为允许的值(XC,YC,r),具有最多接收到的表决票的实例值代表圆的参数。对于瞳孔讲,可允许的参数值被限制位于用于描述虹膜边缘边界的圆内。
更详细地说,对于步骤308中的眼睑边界,使用一个基于梯度的边缘检测器将图像滤波,该检测器调谐的方向有利于水平方向的操作。此方向选择性考虑到下列事实:任何位于虹膜边缘边界内的眼睑部分是几乎水平的(仍然假定头部是竖直的)。上、下眼睑的模型为两条分离的抛物线,即二次弧线。同样地,所检测边缘像素变稀少并变换为允许的值,从而决定参数化的具体值。对于眼睑讲,所检测边界被附加地限制在用于定义虹膜边缘边界的圆内和上、下眼睑被分别地限制在瞳孔之上方或下方。
最后,在将虹膜边界各部分分离出来之后,最后处理步骤310将这些部分分组合起来以划定虹膜本身。将位于瞳孔边界之外,虹膜边缘边界之内,上眼睑之下和上眼睑之上的图像部分看作虹膜。
上述虹膜定位方案可以多种方式推广。首先,定向性基于梯度的边缘检测以外的其它图像表示法也可用于增强虹膜边界。其次,可采用其它的虹膜边界参数化方法。第三,虹膜边界各部分(虹膜边缘、瞳孔和眼睑边界)的定位可按不同顺序也可并行地完成。第四,在规定虹膜边界各部分的相对图形时可以强调其它限制条件或不强调限制条件。第五,在拟合虹膜边界的参数化模型时可跨越多种分辨率;例如可用迭代的由粗及精方式。第六,在虹膜边界定位中可不用空间求平均值和二次采样的初始步骤。
上述用于将眼部的输入图像中虹膜定位方法(尤其是图3中所示顺序数据处理步骤的示例)的优点是它不需任何附加的初始条件和它可应用简单滤波操作(用于增强有关的图像结构)和曲线变换操作(其中包含用于自增强的图像中复原虹膜边界的表决方案)而不引起计算开销。
图1中自定位装置102输出的只代表定位的用户虹膜图像的处理过的数据作为第一输入量送入匹配装置104,与此同时,先前存储于数据库中的只代表用户声称就是他或她本人的定位的虹膜图像的模型的所选数据作为第二输入量送入匹配装置104。装置104应用本发明原理以有效地将送给它的第一和第二输入数据进行处理,从而确定是否有足够的匹配可以标明该用户事实上就是他或她声称的本人。
更具体地说,人类虹膜的明显空间特性在不同尺度上是明显的。例如,明显的结构包括自虹膜总形状至微小囊窝和详细纹理的分布这些内容。为获取空间结构的这些内容,虹膜图像用一种2D通频带信号分解来表示。初步的经验研究得到的结论是虹膜图像间的可接受的鉴别能以在四种不同分辨率下计算的信频带宽为基础,这是依靠Laplace锥体获取此信息而实现的。由于较低频带连续地二次采样而不损失信息,因此得到有效的存储和处理。
为详细地比较两个图像,最好在一对图像的特性结构之间建立起精确的对应点。一种基于面积的图像对准技术用于此目的。此技术寻找映象函数(u(x,y),v(x,y)),其中对于所有(x,y)值而言,在数据图像中(x,y)、(u(x,y),v(x,y))处的像素值接近模型图像中(x,y)处的值。此处(x,y)是在用所描述的虹膜定位技术将虹膜定位的图像区域内所取。此外,该映射函数被约束为一个相似性变换,即平行移动、定尺度和旋转。这允许对同一虹膜的不同图像事例之间的所观察的自由度进行补偿。平行移动补偿在平行于成像器传感器阵列的平面内的位移偏差。定尺度补偿沿摄像机光轴的位移偏差。旋转补偿未能由眼球旋转扭矩所自然地补偿的围绕光轴的转动偏差。如上所述地与图像检测一起使用的将试图存取者准确地定位的方法中以上这些自由度是在建立对应点中唯一需要调整的自由度。此方案的依据是采用技术中已知的基于模型的动作估值的层次型基于梯度图像对准算法。算法的初始条件由用以上描述的虹膜定位技术定位的虹膜边界的相对偏移距离中导出。
在将模型图像和数据图像准确地和精确地对准后,下一步工作是指定一个用于将比较结果定量化的匹配优度。已知系统对模型图像和数据图像实行精细对准的能力后,可在图像表示的每个频带内将像素差别在空间位置上积分作为一个恰当的匹配度量的依据。空间相关运算可获取此特性。更具体地说是使用归一化相关运算。归一化相关运算将相同类型信息作为标准相关加以获取;然而,如技术中已知的,它也考虑到会使标准相关运算变坏的图像强度的局部变化。相关运算是在每个空间频带内在小块像素上(8×8)完成的。将每个频带的块相关值通过中值统计计算组合起来,即可导出一个匹配优度。将块组合与中值运算一起使用,可对匹配和外围体检测程度进行局部调整,从而针对由噪音、对不准和遮挡(如杂散的睫毛)引起的匹配失误提供鲁棒性。
最后必须完成的工作是将四个已经计算出来的匹配优度值(每个空间频带有一个)组合为一个最终裁决,以确定数据图像是否来自模型图像的同一个虹膜。对这点的合理方案是以下列方式将数值组合起来:使同一类虹膜图像(即同一虹膜的不同事例)间的方差值最小,而使虹膜图像的不同类(即不同虹膜的事例)之间的方差值最大。一个提供这一解法的线性函数是熟知的,它由Fisher线性判别式给出。在一些资料中,此技术在1973年John Wiley & sons印刷的Duda和Hart所写“模式分类与情景分析”一书的114-118页上所介绍。虽然不是任何线性函数都能恰当地区分任意数据集的不同类,但在实际中发现它能很好地用于虹膜图像的处理。此外,在实际中Fisher线性判别式是根据虹膜图像训练数据的小集(包括10个虹膜的5个图像)定义的。在实际中此函数能很好地在具有相应的数据库项目和不具有数据库项目的输入的数据图像之间进行鉴别。
应理解此处介绍的设备和操作方法是为了阐述本发明。熟悉技术的人可在不背离本发明的实质和范围的情况下容易地进行修改。具体讲,可使用相似性以外的对准方法。除自各向同性频带滤波技术中导出者外,其它图像表示也可用作相关运算的基础。例如,可用定向频带滤波或形态学滤波,前者在于1994年6月28日授权予Burt等人的美国专利号5,325,449中被公开。除频带分解外的其它信号分解方法也可使用,如次波分解。次波分解是一种特定的多分辨率锥体类型,它使用正交镜面滤波器(QMF)产生一个原始图像代表性视频信号的次频带分解。一个这种类型的信号处理器描述于1991年4月8-11日在美国加州Los Alamitos的IEEE Computer Society Press的DCC’91数据压缩会议的论文集中由Pentland等人发表的“基于碎片的图像压缩实用方案”一文中。Pentland等人的压缩系统试图使用低频粗略尺度信息以预测高频较细尺度处的明显信息。QMF次频带锥体处理也描述于1991年Kluwer Academic Publishers出版由J.W.Woods编辑的“次频带图像编码”一书中。另外方案是在1994年6月28日授权予Burt等人的美国专利号5,325,449中公开的定向性频带。
图像匹配可用更为符号的方式完成。例如,多个导出的匹配值可用由Fisher线性判别式给出的方式以外的方式组合起来。例如,可用非线性组合(例如用神经元网络导出)也可使用相关运算以外的其它比较方法和除Fisher线性判别式以外的其它决策准则。
可用其它方法将被比较的虹膜对准。例如可将较简单或较复杂的图像变换后的图像对准。在实际匹配过程之前可将环形虹膜图像转换为长方形格式,例如将半径和角度位置转换为垂直和水平坐标。这些操作可用于简化一些随后的操作。在实际匹配过程之前可将虹膜图像沿某一方向投影以产生一维信号。例如,该图像可沿半径方向投影。
本发明可由于控制对一个区域、设施或一个诸如计算机或ATM那样的设备的存取,也可用于生物统计学评价。
Claims (36)
1.一个包括一个具有一个用于获取系统用户眼球聚焦图像的透镜的成像器的系统,其改进包括:
用于允许所述用户自己将他或她的眼球放置于所述成像器的视野内而不必与所述系统有任何物理接触的对准装置。
2.权利要求1的系统,其中所述对准装置包括:
具有第一给定尺寸的给定外轮廓线形状并且基本上与所述透镜同心的第一边框装置,所述第一边框装置位于所述透镜前面第一距离处;以及
具有较所述第一给定尺寸小的第二给定尺寸的与所述给定外轮廓线形状在几何上相似的外轮廓线形状的并且基本上与所述透镜同心的第二边框装置,所述第二边框装置位于所述透镜前面比所述第一距离多出一个给定量的第二距离处,所述给定量如下确定:当所述用户眼球被放置于所述透镜前面较所述第二边框装置更远处的位置上而在所述位置上所述第一边框装置的所述外轮廓线基本上全部被所述第二边框装置的所述外轮廓线所遮盖时所述透镜形成所述用户眼球的聚焦图象。
3.权利要求2的系统,其中:
所述第一边框装置的所述给外轮廓线形状是方形;
同时所述第二边框装置的所述几何上相似的外轮廓线形状也是方形。
4.权利要求2的系统,其中所述系统进一步包括:
使用在给定旋转方向内圆形地偏振的漫射光对所述用户眼球照相以获取自所述用户眼球反射而射入所述透镜的漫射光和射入所述透镜的漫散地反射的光的光源装置。
5.权利要求4的系统,其中所述光源装置包括:
一个围绕所述成像器放置以提供照明光的光源阵列。
一个供来自所述光源阵列的所述照明光通过的漫散屏,所述漫散屏具有一个位于相对于所述透镜中部用于允许所述反射光不通过所述漫散屏而到达所述透镜的孔;以及
一个位于所述漫散屏和所述透镜前面用于将到达所述用户眼球的照明光按所述给定旋转方向圆形地偏振的圆形偏光镜。
6.权利要求4的系统,进一步包括:
一个连至所述成像器用于获取代表所述用户眼球的所述聚焦的图像的数字数据的数字帧获取器。
7.权利要求2的系统,其中所述成像器是一个相对的高分辨率窄域成像器及所述对准装置进一步包括:
一个用于获取包括所述用户的预定面部特征的图像信息的连续视频帧的相对的低分辨率广域成像器;
对所述预定面部特征图像信息作出响应、用于确定所述用户眼球在它的所述视频帧中的位置的图像处理装置;以及
对所述低分辨率广域成像器的所述视频帧中所述用户眼球位置的确定作出响应用于允许所述高分辨率窄域成像器获得所述用户眼球的聚焦的图像的与所述图像处理装置相关连的装置。
8.权利要求7的系统,其中:
所述与所述图像处理装置相关连的装置包括一个根据所述相对的低分辨率广域成像器的所述视频帧中所述用户眼球的所述所确定位置而定位的操纵镜。
9.权利要求7的系统,其中所述图像处理装置包括:
一个用于在图像信息的所述连续视频帧中的至少一帧中获取一个多级图像锥体的高斯锥体;以及
对所述图像锥体级作出响应用于获取一个能量变换锥体的装置。
10.权利要求1的系统,其中所述成像器是一个相对的高分辨率窄域成像器及所述改进进一步包括:
一个用于获取包括所述用户的预定面部特征的图像信息的连续视频帧的相对的低分辨率广域成像器;
对所述预定面部特征图像信息作出响应、用于确定所述用户眼球在它的所述视频帧中的位置的图像处理装置;以及
对所述低分辨率广域成像器的所述视频帧中所述用户眼球位置的确定作出响应用于允许所述高分辨率窄域成像器获得所述用户眼球的一个聚焦的图像的与所述图像处理装置相关连的装置。
11.权利要求7的系统,其中:
所述与所述图像处理装置相关连的装置包括一个根据所述相对的低分辨率广域成像器的所述视频帧中所述用户眼球的所述所确定位置而定位的操纵镜。
12.权利要求7的系统,其中所述图像处理装置包括:
一个用于在图像信息的所述连续视频帧中的至少一帧中获取一个多级图像锥体的高斯锥体;以及
对所述图像锥体级作出响应用于获取一个能量变换锥体的装置。
13.权利要求1的系统,其中所述系统的目标是查验所述用户眼球的虹膜;其中所述系统包括一个连至所述成像器用于获取代表所述用户眼球的所述聚焦的图像的数字数据的数字帧获取器;以及所述改进进一步包括:
对来自所述帧获取器的用于显示所述用户眼球的数字数据作出响应用于按下列顺序将所述眼球虹膜定位的图像处理装置:(1)确定用户眼球图像内定义所述虹膜边缘边界的数据,(2)确定所述虹膜边缘边界内定义所述虹膜的瞳孔边界的数据,(3)确定所述虹膜边缘边界内定义所述眼球的上和下眼睑边界的数据,以及(4)然后只使用所述瞳孔边界之外、虹膜边缘边界之内、上眼睑之下和下眼睑之上的数据以便将所述数据划定为其用于显示所述眼球虹膜的部分。
14.权利要求13的系统,其中:
所述图像处理装置包括用于在按所述顺序将所述眼球虹膜定位之前将来自所述帧获取器的所述数字数据低通滤波并二次采样的装置。
15.权利要求13的系统,其中:
所述图像处理装置采用获取增强的图像的图像滤波装置和包含一个用于自所述增强的图像中复原所述虹膜边界的表决方案的曲线变换装置;
其中所述虹膜边界的所述复原操作除代表所述用户眼球的所述聚焦的图像的数字数据外并不需要了解任何初始条件。
16.权利要求13的系统,其中所述用于确定用户眼球图像内定义所述虹膜边缘边界的数据的图像处理装置包括:
在有利于近垂直操作的方向内调谐的用于获取所检测边缘数据的基于梯度的边缘检测滤波装置;以及
对将所述虹膜边缘边界的圆的模型参数化为两个中心坐标XC和YC及其半径r的操作作出响应,用于根据给定图像位置(x,y)的允许值(XC,YC,r)使所述所检测边缘数据变稀少并曲线变换入一个三维空间(XC,YC,r)的装置;
其中具有最多表决票的(XC,YC,r)点用于代表虹膜边缘边界。
17.权利要求13的系统,其中用于确定用户眼球图像内定义所述虹膜的瞳孔边界的数据的图像处理装置包括:
不在任何方向内调谐的用于获取所检测边缘数据的基于梯度的边缘检测滤波装置;以及
对将所述瞳孔边界的圆的模型参数化为两个中心坐标XC和YC及其半径r的操作作出响应,用于根据给定图像位置(x,y)的允许值(XC,YC,r)使所述所检测边缘数据变稀少并曲线变换入一个三维空间(X,YC,r)的装置;
其中具有最多表决票的(XC,YC,r)点用作代表瞳孔边界。
18.权利要求13的系统,其中用于确定用户眼球图像内定义所述虹膜的所述眼球的上和下眼睑的数据的所述图像处理装置包括:
在有利于水平操作的方向内调谐的用于获取所检测边缘数据的基于梯度的边缘检测滤波装置;以及
对将所述眼球的上眼睑边界和下眼睑边界的抛物线模型参数化为二次弧的操作作出响应,用于根据给定图像位置的允许值使所述所检测边缘数据变稀少和曲线变换入一个三维空间以例示参数化具体数值的装置;
其中具有最多表决票的空间点用于代表眼睑边界。
19.权利要求1的系统,其中所述系统的目标是用于安全存取控制的自动虹膜识别;其中所述系统包括一个连至所述成像器用于获取代表所述用户眼球的所述聚焦的图像的数字数据的数字帧获取器;以及其中所述改进所进一步包括的图像处理装置包括:
对来自所述帧获取器的用于显示所述用户眼球的数字数据作出响应,用于将所述数字数据基本上划定为只显示所述用户眼球的所述虹膜的部分的虹膜定位装置;以及
对所述数字数据的所述部分和先前存储的只显示给定个人的眼球虹膜的数字数据作出响应的模式匹配装置,所述模式匹配装置应用归一化空间相关过算以首先在众多空间尺度的每个尺度上将在空间内互相对准的所述用户和所述给定个人的相应虹膜的每项明显空间特性进行比较以便在所述众多空间尺度的每个尺度上定量地确定该空间尺度上的匹配优度值,以及然后根据在所述众多空间尺度中的每一尺度上所定量地确定的优度值的一定组合判定只用于显示所述用户眼球的所述虹膜的所述所划定的数字数据的模式是否与只用于显示所述给定个人的一个眼球的虹膜的所述数字数据相匹配。
20.权利要求19的系统,其中:
所述模式匹配装置包括使用被约束为平行移动、定尺度和旋转的相似性变换的映象函数(u(x,y),v(x,y))的基于面积的图像对准装置,以便对于所有(x,y)值,在只显示所述用户眼球的所述虹膜的所划定的数字数据中(x,y)-(u(x,y),v(x,y))处的数据值接近于只显示所述给定个人的一个眼球的虹膜的所述数字数据的(x,y)处的数据值。
21.权利要求19的系统,其中:
所述模式匹配装置包括用于在由在所述众多尺度的每个尺度的第一空间尺寸内第一众多数据点及由在所述众多尺度的每个尺度的第二空间尺寸内的第二众多数据点所组成的给定空间块上完成归一化空间相关运算的装置。
22.权利要求21的系统,其中:
所述模式匹配装置包括通过所述众多空间尺度的每个尺度处的中值统计计算将在所述给定空间块上完成的所述归一化空间相关运算值组合以定量地确定该空间尺度处的所述匹配优度值的装置。
23.权利要求19的系统,其中:
所述模式匹配装置包括将所述众多空间尺度的每个尺度处的定量地确定的匹配优度值组合以使同一虹膜的不同事例间的方差最小和不同虹膜的不同事例间的方差最大的装置。
24.权利要求23的系统,其中:
所述用于将所述众多空间尺度的每个尺度处的定量地确定的匹配优度值组合的装置将Fisher线性判别式用作为使同一虹膜的不同事例间的方差最小和使不同虹膜的不同事例间的方差最大的线性函数。
25.一种对用于定义一个个人的眼球的一个数字化图像的数字数据作出响应的用于将所述数字数据划定为只定义所述个人的所述眼球的虹膜的部分的图像处理方法;其中所述方法包括一个用于确定位于所述个人眼球的图像之内定义所述虹膜的边缘边界、所述虹膜的瞳孔边界和所述眼球的上和下眼睑边界中至少一个边界的数据的划定步骤;在改进中所述划定步骤包括以下步骤:
(a)对所述虹膜的边缘边界,所述虹膜的瞳孔边界和所述眼球的上和下眼睑边界中的所述一个边界实行图像滤波以获得一个它的增强图像;以及
(b)将所述增强的图像进行曲线变换,其中所述曲线变换步骤包含一个用于自所述增强的图像中复原所述虹膜边界中的所述一条边界的表决方案;
其中所述用于复原所述虹膜边界中的所述一条边界的步骤除定义所述个人的所述眼球的所述数字化图像的数字数据外不需要了解任何初始条件。
26.权利要求25的方法,其中所述划定步骤包括以下顺序的步骤:
(c)首先,确定所述数字数据中用于定义所述虹膜的所述边缘边界的部分;
(d)其次,确定位于所述虹膜边缘边界内的所述数字数据中用于定义所述虹膜的所述瞳孔边界的部分;
(e)第三,确定位于所述虹膜边缘边界内的所述数字数据中用于定义所述眼球的上和下眼睑的部分;以及
(f)第四,只应用所述瞳孔边界之外,所述虹膜边缘边界之内、及上眼睑之下和下眼睑之上的所述数字数据部分以将所述数字数据划定为它的用于显示所述眼球的虹膜的部分。
27.权利要求26的方法,其中步骤(c)包括以下步骤:
(g)应用在有利于近垂直操作的方向内调谐的基于梯度的边缘检测滤波装置以检测边缘数据;以及
(h)根据由其两个中心坐标XC和YC与其半径r参数化的圆作为所述虹膜边缘边界的模型,并根据给定(x,y)图像位置的允许值(XC,YC,r)使所述所检测边缘数据变稀少并曲线变换入一个三维空间(XC,YC,r);
其中具有最多表决票的(XC,YC,r)点用于代表虹膜边缘边界。
28.权利要求26的方法,其中步骤(d)包括以下步骤:
(g)应用不在任何方向内调谐的基于梯度的边缘检测滤波装置以检测边缘数据;以及
(h)根据由其两个中心坐标XC和YC与其半径r参数化的圆作为所述瞳孔边界的模型,并根据给定图像位置(x,y)的允许值(XC,YC,r)使所述所检测边缘数据变稀少并曲线变换入一个三维空间(XC,YC,r);
其中具有最多表决票的(XC,YC,r)点用于代表瞳孔边界。
29.权利要求26的方法,其中步骤(e)包括以下步骤:
(g)应用在有利于水平操作的方向内调谐的基于梯度的边缘检测滤波装置以检测边缘数据;以及
(h)根据由二次弧参数化的抛物线作为所述眼球的上眼睑边界和下眼睑边界的每一条边界的模型,并根据给定图像位置的允许值使所述所检测数据变稀少并曲线变换入一个三维空间;
其中具有最多表决票的空间点用于代表该眼睑边界。
30.权利要求25的方法,进一步包括以下步骤:
在完成所述划定步骤之前将用于定义所述个人的所述眼球的所述数字化图像的所述数字数据低通滤波和二次采样。
31.一种用于提供为安全存取控制提供自动虹膜识别的图像处理方法;所述方法对只用于定义一定试图存取的个人的眼球虹膜的数字化图像的第一数字数据作出响应和对先前存储的只用于定义一个特定个人的眼球虹膜的数字化图像的第二数字数据作出响应;其中所述方法包括的模式匹配步骤包括以下步骤:
(a)应用归一化空间相关运算以便首先在众多空间尺度的每个尺度上将在空间内互相对准的所述给定个人和所述特定个人的相应虹膜的每一个明显的空间特性进行比较,从而在所述众多空间尺度的每个尺度上定量地确定该空间尺度上的匹配优度值;以及
(b)根据所述众多空间尺度的每个尺度上定量地确定的匹配优度值的一定组合判定只用于显示所述给定个人的所述眼球的所述虹膜的所述数字数据模式是否与只用于显示所述特定个人的一个眼球虹膜的所述数字数据相匹配。
32.权利要求31的系统,其中步骤(a)包括以下步骤:
(c)使用一个被约束为平行移动,定尺度和旋转的相似性变换的映象函数(u(x,y),v(x,y))实行基于面积的图像对准,以便对于所有(x,y)值,在只显示所述给定个人的所述眼球的所述虹膜的第一数字数据中(x,y)-(u(x,y),v(x,y))处的数据值接近于只显示所述特定个人的一个眼球虹膜的所述第二数字数据的(x,y)处的数据值。
33.权利要求31的系统,其中步骤(a)包括以下步骤:
(c)在由所述众多尺度的每个尺度的第一空间尺寸内第一众多数据点及由所述众多尺度的每个尺度的第二空间尺寸内的第二众多数据点所组成的给定空间块上完成归一化空间相关运算。
34.权利要求33的系统,其中步骤(c)包括以下步骤:
(d)通过众多空间尺度的每个尺度处的中值统计计算将在所述给定空间块上完成的所述归一化空间相关运算值组合以定量地确定该空间尺度处的所述匹配优度值。
35.权利要求31的系统,其中步骤(b)包括以下步骤:
(c)将所述众多空间尺度的每个尺度处的定量地确定的匹配优度值组合以使同一虹膜的不同事例间的方差最小和不同虹膜的不同事例间的方差最大。
36.权利要求35的系统,其中步骤(c)包括以下步骤:
(d)应用Fisher线性判别式作为使同一虹膜的不同事例间的方差最小和使不同虹膜的不同事例间的方差最大的线性函数。
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CN (1) | CN1160446A (zh) |
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CA (1) | CA2199040A1 (zh) |
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CN103440483A (zh) * | 2013-09-03 | 2013-12-11 | 吉林大学 | 一种主动性对焦的虹膜图像采集装置 |
CN104484649A (zh) * | 2014-11-27 | 2015-04-01 | 北京天诚盛业科技有限公司 | 虹膜识别的方法和装置 |
CN104484649B (zh) * | 2014-11-27 | 2018-09-11 | 北京天诚盛业科技有限公司 | 虹膜识别的方法和装置 |
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AU3419895A (en) | 1996-03-27 |
EP1126403A2 (en) | 2001-08-22 |
MX9701624A (es) | 1998-05-31 |
JP2006260583A (ja) | 2006-09-28 |
JPH10505180A (ja) | 1998-05-19 |
US5572596A (en) | 1996-11-05 |
WO1996007978A1 (en) | 1996-03-14 |
JP2006302276A (ja) | 2006-11-02 |
HUT76950A (hu) | 1998-01-28 |
JP3943591B2 (ja) | 2007-07-11 |
CA2199040A1 (en) | 1996-03-14 |
EP0793833A1 (en) | 1997-09-10 |
EP0793833A4 (en) | 1998-06-10 |
BR9508691A (pt) | 1998-01-06 |
AU702883B2 (en) | 1999-03-11 |
US5751836A (en) | 1998-05-12 |
KR970705798A (ko) | 1997-10-09 |
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