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CN1685357A - Method and apparatus for identifying a palmprint image - Google Patents

Method and apparatus for identifying a palmprint image Download PDF

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Publication number
CN1685357A
CN1685357A CN 03822593 CN03822593A CN1685357A CN 1685357 A CN1685357 A CN 1685357A CN 03822593 CN03822593 CN 03822593 CN 03822593 A CN03822593 A CN 03822593A CN 1685357 A CN1685357 A CN 1685357A
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method
apparatus
identifying
palmprint
image
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CN 03822593
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Chinese (zh)
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CN100380391C (en )
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张大鹏·戴维
钮旋
卢光明
江伟健·亚当
王明强
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香港理工大学
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00006Acquiring or recognising fingerprints or palmprints
    • G06K9/00067Preprocessing; Feature extraction (minutiae)
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00362Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/117Identification of persons

Abstract

一种掌纹识别方法,包括:分析来自手掌图像的区域,以利用该区域获得皮肤表面的纹理数据。 One kind palmprint identification method, comprising: an image analysis region from the palm, in order to obtain the texture data by using the region of the skin surface. 将纹理数据与数据库中的参考信息进行比较,以确定对个体的识别。 The texture data is compared with the reference information in the database to determine the identification of an individual. 一种用于捕获手掌图像的设备,包括:外壳,其中具有窗口;以及设置在所述外壳中的图像传感器和光源,用于通过窗口来捕获图像。 An apparatus for the palm image capture device, comprising: a housing having a window; and an image sensor and a light source disposed in the housing for capturing an image through the window. 在表面上设置凸起。 Projections provided on the surface. 设置所述凸起,以使其位于适当放置在窗口上的手的已知相邻位置,以便捕获包括手的手掌区域的图像。 The projection disposed so as to be placed in the proper window location adjacent to a known hand, in order to capture an image including the hand palm area.

Description

掌纹识别方法和设备 Palmprint identification method and apparatus

技术领域 FIELD

本发明涉及生物统计学识别,更具体地涉及一种分析掌纹以便识别个体的方法。 The present invention relates to biometric identification, and more particularly relates to a method for identifying an individual palmprint analysis. 本发明还涉及用于捕获掌纹图像以便识别个体的设备。 The present invention also relates to an image capturing palmprint to identify an individual device.

背景技术 Background technique

利用掌纹识别作为一种个人识别方法是一种代替指纹的新生物统计学技术。 Using as a personal identification palmprint identification method is an alternative to new fingerprint biometrics. 已知的方法包括分析掌纹,以识别掌纹图像中的奇点、细节和皱纹。 Known methods include palmprint analysis to identify singularity palmprint image, details and wrinkles. 这些已知的方法需要如图1所示的高分辨率图像。 These known methods require high-resolution image shown in FIG. 这可以通过染色的掌纹来获得。 This can be achieved by staining palm. 但是,这样做比较肮脏,并且不能获得实时识别。 However, this relatively dirty, and can not obtain real-time recognition.

为了克服染色掌纹的问题,一些公司已经开发了高分辨率掌纹扫描仪和识别系统。 To overcome the problem of stained palm, some companies have developed a high-resolution scanner and palm print identification system. 但是,这些用于捕获高分辨率图像的设备是昂贵的,且依赖于高性能的计算机来满足实时识别的需要。 However, these devices for capturing high-resolution image is expensive and depends on a high performance computer to meet the needs of real-time identification.

对于上述问题的一种解决方法在于使用低分辨率图像。 One solution to the above problem consists in the use of low-resolution image. 图2示出了与图1相对应的低分辨率图像。 Figure 2 shows a low-resolution image corresponding to FIG. 但是,在低分辨率图像中,不能够容易地观察到奇点和细节,因而,更容易识别的皱纹必须在识别中发挥重要作用。 However, in the lower-resolution images, it can not be readily observed and details singularity, therefore, easier to identify the wrinkles must play an important role in recognition. 但是,从图2中可以注意到,只有小部分周围较为清楚,问题在于,其是否提供了足够的独特性,以便在大量人口中,可靠地识别个体。 However, it can be noted from FIG. 2, only a relatively small portion around the apparent problem that, if it offers a unique enough so that a large number of the population, to reliably identify an individual.

发明内容 SUMMARY

本发明的一个目的在于提供一种生物统计学识别方法,更具体地,一种分析掌纹以便识别个体的方法,其克服或改善了现有方法的缺陷。 An object of the present invention is to provide a method of biometric identification, and more particularly, to a method of identifying an individual palmprint analysis, which overcomes or ameliorates disadvantages of the prior methods. 本发明的另一目的是提供一种用于捕获掌纹图像的设备,其克服或改善了现有设备的缺陷,或者至少其向公众提供了一种有用的可选设备。 Another object of the present invention is to provide an apparatus for capturing an image of the palm, which overcomes or ameliorate the shortcomings of existing equipment, or at least provides a useful alternative to the public device.

根据本发明的第一方面,提出了一种生物统计学识别方法,包括:从个体获得皮肤表面区域的图像;分析所述图像,以提取出皮肤表面区域上的纹理特征;以及将所述纹理特征与数据库中的参考信息进行比较。 According to a first aspect of the present invention, there is proposed a statistical method of identifying organisms, comprising: obtaining an image from an individual surface area of ​​the skin; analyzing the image to extract texture features on the surface of the skin region; and the texture wherein the information is compared with the reference database.

根据本发明的第二方面,提出了一种生物统计学识别方法,包括:获得个体的手的部分内表面的图像;获得手的内表面的指定区域内的皮肤表面的子图像;分析所述子图像,以获得皮肤表面的纹理数据;以及将所述纹理数据与数据库中的参考信息进行比较。 According to a second aspect of the present invention, there is proposed a statistical biological recognition, comprising: obtaining an individual image of the hand portion of the inner surface; sub-images of the skin surface within the inner surface of the hand obtained designated area; analyzing the sub-images, to obtain texture data of the skin surface; and the texture data is compared with a reference information in the database.

优选地,所述指定区域依赖于手的一个或多个特征。 Preferably, wherein said one or more designated areas depends on the hand.

优选地,所述一个或多个特征是手指之间区域。 Preferably, wherein said one or more regions between the fingers.

优选地,通过以下步骤获得所述子图像,包括:识别表示手指之间的区域的至少两个点;确定具有第一和第二轴的坐标系统,其中所述两个点位于所述第一轴上,且与所述第二轴等距;以及利用所述两个点之间的距离,确定所述子图像在所述坐标系统内的参数。 Preferably, the step of obtaining by said sub-image, comprising: identifying at least two points represents the region between the finger; determining coordinate system having a first and a second axis, wherein the two points in the first shaft and equidistant from said second axis; and using the distance between the two points, determining the parameters of the sub-image within the coordinate system.

优选地,所述子图像的所述参数包括所述坐标系统中、以(0.25D,0.5D)、(1.25D,0.5D)、(0.25D,-0.5D)和(1.25D,-0.5D)表示的点,其中D是所述两个点之间的距离。 Preferably, the parameter comprises the sub-image coordinate system to (0.25D, 0.5D), (1.25D, 0.5D), (0.25D, -0.5D) and (1.25D, -0.5 D) represented by a point, where D is the distance between the two points.

优选地,还包括对所述子图像进行规一化的步骤。 Preferably, further comprising the step of normalization of the sub-image.

优选地,分析所述子图像包括利用伽柏滤波器。 Preferably, the sub-image analysis comprises using Gabor filters.

优选地,分析所述子图像包括利用伽柏分析以低分辨率分割所述子图像的层。 Preferably, the sub-image analysis using Gabor analysis comprises dividing the low-resolution image of the sub-layer.

优选地,将所述子图像分割为两个部分,实部和虚部,将每一部分存储为向量。 Preferably, the sub-images is divided into two parts, the real and imaginary parts, each part will be stored as a vector.

优选地,将所述纹理数据与数据库中的参考信息进行比较基于以下形式的汉明距离: Preferably, the texture data is Hamming distance based on a comparison of the form with the reference information in a database:

D0=Σi=1NΣj=1NPM(i,j)∩QM(i,j)((PR(i,j)⊗QR(i,j)+PI(i,j)⊗QI(i,j)))2Σi=1NΣj=1NPM(i,j)∩QM(i,j),]]>其中PR(QR)和PI(QI)和是所述实部和所述虚部。 D0 = & Sigma; i = 1N & Sigma; j = 1NPM (i, j) & cap; QM (i, j) ((PR (i, j) & CircleTimes; QR (i, j) + PI (i, j) & CircleTimes; QI (i, j))) 2 & Sigma; i = 1N & Sigma; j = 1NPM (i, j) & cap; QM (i, j),]]> where PR (the QR) and PI (QI) and said real portion and the imaginary part.

根据本发明的第三方面,提出了一种掌纹图像捕获设备,包括:外壳,其中具有窗口;设置在所述外壳中的图像传感器,并通过窗口来捕获图像;光源,用于照亮所述窗口;以及与所述窗口相邻的至少一个凸起,其中设置所述凸起,以使其位于适当放置在窗口上的手的已知相邻位置,以便捕获包括手的手掌区域的图像。 According to a third aspect of the present invention there is provided a palm image capturing apparatus, comprising: a housing having a window; illuminated by a light source, for; an image sensor disposed in the housing and captures an image through the window said window; and adjacent to the window at least one projection, wherein the projection is provided so as to be placed in the proper window location adjacent to a known hand, to capture the image of the hand palm area comprising .

根据本发明的第四方面,提出了一种掌纹图像捕获设备,包括:外壳,其中具有窗口;设置在所述外壳中的图像传感器,并通过窗口来捕获图像;光源,用于照亮所述窗口;控制器,用于控制所述图像传感器和光源,以便捕获图像;以及与所述窗口相邻的至少一个凸起,其中设置所述凸起,以使其位于适当放置在窗口上的手的已知相邻位置,以便捕获包括手的手掌区域的图像。 According to a fourth aspect of the present invention there is provided a palm image capturing apparatus, comprising: a housing having a window; illuminated by a light source, for; an image sensor disposed in the housing and captures an image through the window said window; a controller for controlling the image sensor and a light source, so as to capture an image; and a window adjacent to said at least one projection, wherein the projection is provided so as to be positioned on the proper placement of the window hand known adjacent positions, to capture the image including the hand palm area.

优选地,所述凸起是设置在适当放置在窗口上的手的两个或多个手指之间的栓或销。 Preferably, the projection is a pin or bolt is provided between two properly positioned on the window or more fingers of the hand.

优选地,所述光源是所述图像传感器位于其中心的环面。 Preferably, the light source is an annulus of the image sensor at the center thereof.

优选地,所述图像传感器是电荷耦合器件(CCD)或互补金属氧化物半导体(CMOS)传感器。 Preferably, the image sensor is a charge coupled device (CCD) or complementary metal oxide semiconductor (CMOS) sensor.

通过仅作为示例给出的以下描述,本发明的其他方面将变得显而易见。 Following description given by way of example only, and other aspects of the present invention will become apparent.

附图说明 BRIEF DESCRIPTION

现在,将参照附图,对本发明的实施例进行描述,其中: Now, with reference to the drawings, embodiments of the present invention will be described, wherein:

图1示出了典型的高分辨率掌纹图像;图2示出了典型的低分辨率掌纹图像;图3到图8示出了对手内侧图像的预处理;图9和图10示出了手在手掌读取器上的不正确放置及相应的预处理图像;图11到图14示出了预处理图像、实部和虚部以及掩模;图15和图16示出了第一和第二收集图像之间图像质量的差别;图17和图18示出了根据本发明的方法的验证测试结果;图19示出了根据本发明的掌纹图像捕获设备的示意图;图20示出了该设备的图像捕获表面的平面图;图21是沿图19中的A-A'得到的剖面图,其中CCD摄像机沿圆形光旋转;以及图22示出了由设备所捕获的原始手掌图像。 FIG 1 illustrates a typical high-resolution palmprint image; FIG. 2 shows a typical low-resolution palmprint image; FIG. 3 to FIG. 8 illustrates the inside of the opponent preprocessing image; FIG. 9 and FIG. 10 shows the the hand is not placed on the palm of the right reader and the corresponding pre-processed image; FIG. 11 to FIG. 14 shows the image pre-processing, the real and imaginary part and the mask; FIG. 15 and FIG. 16 illustrate a first and the difference between the second image quality captured images; Fig. 17 and FIG. 18 shows the results of verification testing method according to the present invention; FIG. 19 shows a schematic view of a palm image capturing apparatus according to the present invention; FIG. 20 shows a plan view of the image capturing surface of the apparatus; FIG. 21 is taken along in FIG. 19 a-a 'cross-sectional view taken, along which circular light CCD camera rotation; and FIG. 22 shows an original palm captured by the device image.

具体实施方式 detailed description

本发明的掌纹识别方法包括三个部分:1)获得个体的掌纹图像;2)根据该图像,分析皮肤纹理数据;以及3)将皮肤纹理数据与存储在数据库中的信息进行比较。 Palmprint identification method of the invention comprises three parts: 1) to obtain palmprint individual; 2) based on the image data of skin texture analysis; and 3) the skin texture information data stored in the database for comparison. 下面,将更为详细的描述这些步骤。 Hereinafter, these steps will be described in more detail.

1)获得个体的掌纹图像参考图3,利用CCD摄像机,按照已知的方式来获得手的部分内表面的低分辨率图像。 1) obtaining a subject image palmprint reference to Figure 3, a CCD camera, in a known manner to obtain a low resolution image portion of the inner surface of the hand. 为了从图像中提取出识别数据,必须利用手的特征来识别手掌区域的可重复子图像。 Sub-image may be repeated in order to extract the identification data from the image, the hand must be used to identify features of the palm region. 在优选实施例中,识别手指间的缺口,并用作构建坐标系统的参数,可以在所述坐标系统中找出定义了子图像的参数。 Embodiment, the gap between the fingers identified in the preferred embodiment, and as building parametric coordinate system, can find out the parameters defined in the sub-image coordinate system. 优选实施例具有六个主要步骤,如以下所述。 A preferred embodiment has six major steps, as described below.

参照图4,第一步是对原始图像0(x,y)应用低通滤波器L(u,v),如高斯滤波器等。 Referring to FIG. 4, the first step is the original image 0 (x, y) low-pass filter L (u, v), such as a Gaussian filter or the like. 然后,利用阈值Tp,将卷积的图像转换为二值化图像B(x,y)。 Then, using the threshold value Tp of, the convoluted image is converted to binarized image B (x, y).

参照图5,第二步是利用边界跟踪算法,获得手指之间的缺口的边界(Fixj,Fiyj):其中i=1、2。 Referring to FIG 5, the second step is tracking algorithm using the boundary, a boundary gap is obtained between the finger (Fixj, Fiyj): where i = 1,2. 并不提取出无名指与中指之间的缺口的边界,由于其对于以下处理没有用。 It does not extract the boundary gap between the ring finger and the middle finger, because it is not used for the following processing.

参照图6,第三步是计算缺口(Fixj,Fiyj)的切线。 Referring to FIG. 6, the third step is to calculate the gap (Fixj, Fiyj) tangent. 如果(x1,y1)和(x2,y2)分别是(F1xj,F1yj)和(F2xj,F2yj)上的两个点,则对于所有的i和j,通过这两个点的直线(y=mx+c)满足不等式Fiyj≤mFixj+C。 If (x1, y1) and (x2, y2) are (F1xj, F1yj) and (F2xj, F2yj) on two points, then for all i and j, the two points by a straight line (y = mx + c) satisfy the inequality Fiyj≤mFixj + C. 直线(y=mx+c)是两个缺口的切线。 A straight line (y = mx + c) is tangent two notches. 以图6中的数字2表示的这条直线是坐标系统的Y轴,用于确定子图像1的位置。 This straight line in FIG. 6 numeral 2 is a Y-axis coordinate system, for determining the position of a sub-image.

第四步是找出通过两个点的中点、垂直于直线2的直线3,以确定坐标系统的X轴和原点。 The fourth step is to find the midpoint by two points, the straight line perpendicular to the straight line 2 3, to determine the X axis and the origin of the coordinate system. 所述两个点位于Y轴上,且与X轴等距。 The two points on the Y-axis, the X axis and equidistant.

第五步是根据坐标系统提取出具有动态尺寸的子图像1。 The fifth step is to extract a sub-image having a dynamic size in accordance with the coordinate system. 子图像1的尺寸和位置基于两个点(x1,y1)和(x2,y2)之间的欧几里得距离(D)。 Euclidean distance between the sub-image size and position 1 based on two points (x1, y1) and (x2, y2) (D). 坐标系统中表示子图像1的角的点4、5、6、7分别是(0.25D,0.5D)、(1.25D,0.5D)、(0.25D,-0.5D)和(1.25D,-0.5D)。 Sub-image coordinate system representing a corner point are 4,5,6,7 (0.25D, 0.5D), (1.25D, 0.5D), (0.25D, -0.5D) and (1.25D, - 0.5D). 因此,子图像1是每条边均等于欧几里得距离且关于Y轴直线3对称的正方形。 Thus, the sub-picture 1 are both equal to the Euclidean distance of each edge and symmetrical about the Y axis linear 3 square. 因为子图像依赖于手的特征(手指之间的区域),其对于每个个体的手而言是可重复的。 Because the sub-images depends on the characteristics of the hand (the area between the fingers), which is repeated for each individual in terms of hand.

图7示出了坐标系统的x和y轴2、3以及重叠在图3的原始图像上的子图像1。 FIG 7 illustrates a coordinate system x and y axes 2, 3 and superimposed on the original image of FIG. 3 1 sub-images.

第六步是利用双线性插值对子图像1进行提取并规一化为标准尺寸。 A sixth step is extracted and normalized to a standard size sub image using bilinear interpolation. 图8示出了所提取出的规一化子图像1。 FIG 8 shows a gauge 1 facilitator extracted image.

在获得手掌子图像1时,进行本方法的下一部分。 Sub-image is obtained when the palm 1, a part of the process carried out.

2)分析图像的皮肤纹理循环伽柏滤波器是用于纹理分析的有效工具,并具有以下一般形式:G(x,y,θ,u,σ)=12πσ2exp{-x2+y22σ2}exp{2πi(uxcosθ+uysinθ)}---(1)]]>其中i=-1;]]>u是正弦波的频率;θ控制函数的方向,以及σ是高斯包络的标准偏差。 2) skin texture cyclic Gabor filter analysis of the images is an effective tool for texture analysis are used, and has the following general form: G (x, y, & theta;, u, & sigma;) = 12 & pi; & sigma; 2exp {-x2 + y22 & sigma; 2} exp {2 & pi; i (uxcos & theta; + uysin & theta;)} --- (1)]]> where i = -1;]]> u is the frequency of the sine wave; direction θ of the control function, and σ is the standard deviation of the Gaussian envelope. 伽柏滤波器广泛地用在纹理分析中,因此,本领域的普通技术人员将熟悉其针对这种目的的应用。 Gabor filters widely used in texture analysis, therefore, those of ordinary skill in the art will be familiar with its use for this purpose.

为了使纹理分析对图像亮度的变换更为稳定,通过应用以下公式,将离散伽柏滤波器G[x,y,θ,u,σ]变为零DC:G~[x,y,θ,u,σ]=G[x,y,θ,u,σ]-Σi=-nnΣj=-nnG[i,j,θ,u,σ](2n+1)2---(2)]]>其中(2n+1)2是滤波器的尺寸。 In order to texture analysis of image brightness conversion is more stable, by applying the following equation, a discrete Gabor filter G [x, y, θ, u, σ] becomes zero DC: G ~ [x, y, & theta; , u, & sigma;] = G [x, y, & theta;, u, & sigma;] - & Sigma; i = -nn & Sigma; j = -nnG [i, j, & theta;, u, & sigma;] (2n + 1 ) 2 --- (2)]]> where (2n + 1) 2 is the size of the filter. 事实上,因为奇对称,伽柏滤波器的虚部自动具有零DC。 In fact, because the odd symmetry, the imaginary part of the Gabor filter having an automatic zero DC. 调整后的伽柏滤波器的用途是对预处理图像进行滤波。 Use adjusted Gabor filter is a pre-filtered image. 然后,通过以下不等式,对相位信息进行编码:br=1如果Re(Σy=-nnΣx=-nnG~[x,y,θ,u,σ]I(x+x0,y+y0))≥0,---(3)]]>br=0如果Re(Σy=-nnΣx=-nnG~[x,y,θ,u,σ]I(x+x0,y+y0))<0,---(4)]]>bi=1如果Im(Σy=-nnΣx=-nnG~[x,y,θ,u,σ]I(x+xo,y+yo))≥0,---(5)]]>bi=0如果Im(Σy=-nnΣx=-nnG~[x,y,θ,u,σ]I(x+xO,y+yO))<0,---(6)]]>其中I(x,y)是预处理图像,以及(x0,y0)是滤波中心。 Then, phase information is performed by the following inequality encoding: br = 1 if Re (& Sigma; y = -nn & Sigma; x = -nnG ~ [x, y, & theta;, u, & sigma;] I (x + x0, y + y0)) & GreaterEqual; 0, --- (3)]]> br = 0 if Re (& Sigma; y = -nn & Sigma; x = -nnG ~ [x, y, & theta;, u, & sigma;] I ( x + x0, y + y0)) & lt; 0, --- (4)]]> bi = 1 if Im (& Sigma; y = -nn & Sigma; x = -nnG ~ [x, y, & theta;, u, & sigma;] I (x + xo, y + yo)) & GreaterEqual; 0, --- (5)]]> bi = 0 if Im (& Sigma; y = -nn & Sigma; x = -nnG ~ [x, y, & theta;, u, & sigma;] I (x + xO, y + yO)) & lt; 0, --- (6)]]> where I (x, y) is pre-processed image, and (x0, y0) It is the filter center.

参照图9和图10,由于可以预期一些用户会不正确地放置他们的手,一些非掌纹像素将包含在手掌子图像中。 Referring to FIGS. 9 and 10, since it is contemplated that some users may not properly placed their hands, palm non-sub-pixels included in the image of the palm. 产生了掩模以指出非掌纹像素的位置。 Generating a mask to indicate the position of the non-pixel palm. 因为可以认为图像源是半封闭环境,非掌纹像素来自图像背景的黑色边界。 Because the image source can be considered a semi-enclosed environment, from the non-pixel black border palmprint image background. 因此,使用阈值来分割非掌纹像素。 Thus, using the threshold value to the non-divided pixels palmprint. 典型地,包括掩模和掌纹特征的特征尺寸为384字节。 Typically, a mask and palm features including feature size is 384 bytes.

图11示出了预处理图像,图12示出了相应纹理特征的实部,图13示出了相应纹理特征的虚部,以及图14示出了相应掩模。 FIG. 11 shows the image pre-processing, FIG. 12 shows the real part of a corresponding texture feature, FIG. 13 shows the imaginary part of the corresponding texture feature, and Figure 14 shows the corresponding mask.

可以在以下两个公开文件中找到将伽柏滤波器用于纹理分析的有益讨论。 The discussion will be found useful Gabor filters for texture analysis in the following two public documents. A.Jain和G.Healey发表在IEEE Transactions on ImageProcessing、1998年第7卷第1号、第124~128页上的、题为“Amultiscale representation including opponent color features fortexture recognition”的文章。 A.Jain and G.Healey published in IEEE Transactions on ImageProcessing, 1998, Vol. 7, No. 1, entitled "Amultiscale representation including opponent color features fortexture recognition" of the article on page 124 ~ 128. 以及D.Dunn和WEHiggins发表在IEEE Transactions on Image Processing、1995年第4卷第4号、第947~964上的题为“Optimal Gabor filters for texturesegmentation”的文章。 And D.Dunn and WEHiggins published in IEEE Transactions on Image Processing, 1995, Vol. 4 No. 4, entitled on the first 947 ~ 964 "Optimal Gabor filters for texturesegmentation" article.

3)掌纹匹配将实部和虚部特征表示为矢量,将其与所存储的掌纹数据的矢量进行比较。 3) palmprint matching the real and imaginary parts represented as a feature vector, which is compared with the vector data stored palm. 掌纹匹配基于规一化的汉明距离。 Palmprint matching based on a normalized Hamming distance. 例如,P和Q是两个掌纹特征矩阵,规一化的汉明距离可以描述为:D0=Σi=1NΣj=1NPM(i,j)∩QM(i,j)((PR(i,j)⊗QR(i,j)+PI(i,j)⊗QI(i,j)))2Σi=1NΣj=1NPM(i,j)∩QM(i,j),---(7)]]>其中PR(QR)、PI(QI)和PM(QM)分别是P(Q)的实部、虚部和掩模;当且仅当两个比特PR(I)(i,j),等于QR(I)(i,j)时,布尔操作“”的结果等于零;∩表示与操作,以及特征矩阵的尺寸为N×N。 For example, P and Q are two palm characteristic matrix, the normalized Hamming distance may be described as: D0 = & Sigma; i = 1N & Sigma; j = 1NPM (i, j) & cap; QM (i, j) (( PR (i, j) & CircleTimes; QR (i, j) + PI (i, j) & CircleTimes; QI (i, j))) 2 & Sigma; i = 1N & Sigma; j = 1NPM (i, j) & cap; QM (i , j), --- (7)]]> where PR (QR), PI (QI) and PM (QM) are P (Q) of the real part and the imaginary part of the mask; if and only if two bit PR (I) (i, j), equal to QR (I) (i, j), the result "" Boolean operators zero; ∩ denotes the operation size, and wherein the matrix is ​​N × N. 应当注意,D。 It should be noted, D. 在1和0之间。 Between 0 and 1. 对于完全匹配,匹配得分为零。 For exact match, the matching score is zero. 因为不完全的预处理,需要对特征进行垂直和水平转换,然后再进行匹配。 Because of incomplete pretreatment, the need for vertical and horizontal conversion characteristics, and then matched. 于是,垂直和水平转换的范围是-2到2。 Thus, conversion of the vertical and horizontal range of -2 to 2. 将通过转换匹配而获得的D。 D. will be obtained by conversion match 的最小值作为最终匹配得分。 The minimum value as the final match score.

以下的实验结果描述了本发明系统的有效性。 The following experiments describe the effectiveness of the system of the present invention.

利用掌纹扫描仪从154个对象收集掌纹图像。 Palm scanner using palmprint collected from 154 objects. 大约65%的对象是男性。 About 65% of subjects were male. 对象的年龄分布如表1所示。 The age distribution of the object as shown in Table 1.

每个对象提供两组图像。 Each object provides two sets of images. 每组包含左手掌的10幅图像和右手掌的10幅图像。 Each group contains 10 images, and 10 images the palm of the left hand. 总共,每个对象提供40幅图像,以创建包含来自于308个不同手掌的6191幅图像的图像数据库。 In total, each object provides 40 images to create an image that contains from 308 different image database 6191 palm. 从每个对象收集第一和第二组图像之间的平均时间间隔为57天。 The average time interval between the collection of each object image of the first and second set of 57 days. 最大和最小时间间隔分别为90和4天。 The maximum and minimum time interval of 90 and 4 days respectively. 在完成第一次收集之后,改变光源,并将焦点调节到CCD摄像机上,从而通过两个不同的掌纹扫描仪来模拟图像收集。 After completion of the first collection, light source is changed, and the focus is adjusted on the CCD camera, thereby simulation image collected by two different palm scanner. 图15和16示出了在针对一个对象的第一和第二组中所捕获的相应手图像。 Figures 15 and 16 illustrate in respective first and second set of hand images captured for one object. 所收集的图像具有两种尺寸:384×284和768×568。 The collected image has two dimensions: 384 × 284 and 768 × 568. 将较大的图像的尺寸调整为384×284;因此,以下实验中的所有测试图像的尺寸为384×284,分辨率为75dpi。 The larger resized image is 384 × 284; thus, the size of all the experiments the following test image is 384 × 284, a resolution of 75dpi.

为了获得掌纹系统的验证精度,将每个掌纹图像与数据库中的所有掌纹图像进行匹配。 In order to obtain palmprint verify the accuracy of the system, each image is matched with all the palm palmprint image database. 将匹配标记为来自相同对象的相同手掌的两个掌纹图像的正确匹配。 The match marked as correct matching of two identical palm palmprint images from the same object. 比较总数为19161145。 Comparison to the total number of 19,161,145. 正确匹配数为59176。 Correct matching number 59176.

分别通过正确和不正确匹配来估计真实的和冒名顶替的概率分布示于图17。 Respectively correct and incorrect estimate the probability of matching the real and impostor distribution is shown in FIG. 17. 图18示出了相应的接受操作曲线(ROC),是针对所有可能操作点的真实接受比率对错误接受比例的曲线。 FIG. 18 shows a corresponding receiving operating curve (ROC), is the true acceptance rate for all possible operating points of the false acceptance ratio curve. 根据图18,可以估计根据本发明的方法可以以96%的真实接受比率和0.1%的错误接受比率进行操作;相应的阈值为0.35。 According to FIG. 18, can be estimated according to the method of the present invention is acceptable to the true acceptance rate of 96% and 0.1% error rate of operation; corresponding threshold is 0.35. 此结果可以与现有掌纹解决方案和包括手几何学和指纹验证在内的其他基于手的生物统计学技术相比。 This result can be the solution with existing palm and includes a hand geometry and fingerprint verification, including on hand compared to other biometric technologies.

根据本发明的方法利用低分辨率图像,并具有较低的计算成本。 Low resolution image using a method according to the present invention, and has a lower computational cost. 验证精度可以与使用高分辨率图像的高性能方法相比。 Verify the accuracy comparable to a high-performance high-resolution image using the method.

此方法可以用于接入控制、ATM和多种安全系统。 This method can be used for access control, ATM, and a variety of security systems.

图19和20示出了根据本发明的掌纹图像捕获设备。 19 and 20 illustrate an image captured in accordance with the present invention palmprint apparatus. 所述设备包括外壳1,具有平坦的上表面2,将手放置在其上,手掌向下,以便捕获掌纹图像。 The apparatus comprises a housing 1 having a flat upper surface 2, on which the hand is placed palm down, in order to capture palm print images. 表面2是不透明的,具有通过其捕获图像的窗口8。 2 the surface is opaque, with a window 8 through which a captured image. 在优选实施例中,窗口8包括玻璃板。 In the preferred embodiment, window 8 comprises a glass plate. 在可选实施例中,窗口8可以包含其他透明的遮盖物、透镜或凹口(即,开放窗口)。 In an alternative embodiment, the window 8 may comprise other transparent covering material, lens or notches (i.e., open the window).

将如电荷耦合器件(CCD)4等图像传感器安装在外壳1中。 As the charge coupled device (CCD) 4 and the like in an image sensor mounted in the housing 1. 将透镜5旋紧在CCD上。 5 screwed on the lens CCD. 透镜5的孔径朝向表面2中的窗口8。 5 toward the aperture of the lens surface 2 in the window 8.

安装环形光源6,围绕透镜5,以照亮窗口8中的图像。 An annular light source 6 is mounted around the lens 5, to illuminate the image in the window 8. 安装臂7支撑环形光源6,并使用螺丝钉9将CCD牢固地安装到安装臂7上。 The light source 6 is mounted an annular supporting arm 7, and 9 using a CCD screws securely mounted to the mounting arm 7. 可以通过从透镜5到CCD 4的此光学平面形成掌纹图像,然后将数字化的图像数据传送到如个人计算机(未示出)等外部处理器,以便进行处理和操作。 Palmprint image formed by the lens 5 to the optical plane of the CCD 4, and then transmits the digitized data to the image such as a personal computer (not shown) external to the processor, for processing and operations.

参照图21,示出了通过图19中的截面A-A'的透镜5和光源6的平面图。 Referring to FIG 21, it shows a plan view in FIG. 19 cross-section through A-A 'of the lens 5 and light source 6. 透镜5位于环形光源6的中心。 Lens 5 light source 6 located in the center of the ring. 将透镜5安装在CCD 4的顶部。 The lens 5 is mounted on the top of the CCD 4.

与表面2中的窗口8相邻的是多个凸起,为栓3的形式,用于将手正确地定位在表面2上,使手掌区域位于窗口8的上方。 2 and the surface adjacent to the window 8 are a plurality of projections, in the form of plug 3 for correct positioning of the hand on the surface 2, so the palm is positioned over the window area 8. 在使用时,人们将手放在表面2上,使栓3位于拇指和其他手指之间。 In use, one hand on the upper surface 2, so that the bolt 3 is located between the thumb and fingers. 这样确保手正确地放置在设备上,以便通过窗口8来捕获手掌的最佳区域。 This ensures proper hand placed on the apparatus, so as to capture the best palm region 8 through the window.

图22示出了通过窗口8捕获的目标手掌区域的图像。 FIG. 22 shows an image obtained by capturing a target region 8 a palm window. 显而易见的是,使用具有目标窗口8的不透明表面2确保能够相应地获得手掌上感兴趣的区域。 Apparent that an opaque window having a target surface 8 2 can be obtained accordingly to ensure that the region of interest on the palm. 个人计算机从CCD 4获得此图像,以进行进一步的处理。 This image is obtained from the personal computer CCD 4, for further processing.

由所述设备获得的掌纹适合于用在生物统计学识别中。 Palm obtained by the device is adapted for use in a biometric identification. 可以获得掌纹的特征和特性,然后,与数据库记录进行比较,以识别个体。 Features and characteristics can be obtained in the palm, and then, compared with the database records to identify an individual. 多种技术可以用于确定图像中的手掌的特性。 A variety of techniques may be used to determine the characteristics of the image of the palm. 一种适合的技术是纹理分析。 A suitable technique is texture analysis. 纹理分析是合适的,因为其能够基于低分辨率图像给出较高的精度。 Texture analysis is suitable, because it can give a higher accuracy based on the low-resolution image.

所描述的实施例使用了CCD图像传感器。 The described embodiments uses a CCD image sensor. 在可选实施例中,使用互补金属氧化物半导体(CMOS)传感器。 In an alternative embodiment, a complementary metal oxide semiconductor (CMOS) sensor. CMOS传感器以更低的成本产生更低的分辨率。 CMOS sensor at a lower cost to produce a lower resolution. 但是,如果使用纹理分析,则能够对其加以改善。 However, if texture analysis, then it can be improved.

在优选实施例中,与窗口8相邻的凸起是栓3。 In a preferred embodiment, the projection is adjacent to the window 8 bolt 3. 在可选实施例中,以其中能够手掌向下地放置手的凹陷或凹形来形成具有窗口8的表面2。 In an alternative embodiment, in which a hand can be placed palm down depression or concave surface 2 is formed with a window 8.

该设备可以用于捕获用在上述方法中的图像。 The apparatus may be used in the above method for image capturing.

在前述描述中,以相同的整数或元件来表示已知等价物,如这里单独声明的那样,也包括这些等价物。 In the foregoing description, the same elements or represent an integer of known equivalents, as used herein alone stated above, also includes these equivalents.

已经对本发明的实施例进行了描述,但是应当理解,可以进行改变、改进或修改,而并不偏离本发明的精神或所附权利要求的范围。 Example embodiments of the present invention have been described, it is to be understood that changes, modifications or improvements, without departing from the spirit or scope of the invention claimed in the appended claims.

Claims (19)

  1. 1.一种生物统计学识别方法,包括:从个体获得皮肤表面区域的图像;分析所述图像,以提取出皮肤表面区域上的纹理特征;以及将所述纹理特征与数据库中的参考信息进行比较。 A biometric identification method, comprising: obtaining an image from an individual surface area of ​​the skin; analyzing the image to extract texture features on the surface of the skin region; and with the texture feature of the reference information in the database Compare.
  2. 2.一种生物统计学识别方,包括:获得个体的手的部分内表面的图像;获得手的内表面的指定区域内的皮肤表面的子图像;分析所述子图像,以获得皮肤表面的纹理数据;以及将所述纹理数据与数据库中的参考信息进行比较。 A side biometric identification, comprising: obtaining an image of the surface of the inner portion of the subject's hand; sub-images within a designated skin surface area of ​​the inner surface of the hand is obtained; analyzing said sub-image, to obtain a skin surface texture data; and the texture data is compared with a reference information in the database.
  3. 3.根据权利要求2所述的方法,其特征在于所述指定区域依赖于手的一个或多个特征。 3. The method according to claim 2, characterized in that one or more characteristics of the regions depends on the specified hand.
  4. 4.根据权利要求2或3所述的方法,其特征在于所述一个或多个特征是手指之间区域。 4. The method of claim 2 or claim 3, characterized in that said one or more features is the area between the fingers.
  5. 5.根据前述权利要求之一所述的方法,其特征在于通过以下步骤获得所述子图像,包括:识别表示手指之间区域的至少两个点;确定具有第一和第二轴的坐标系统,其中所述两个点位于所述第一轴上,且与所述第二轴等距;以及利用所述两个点之间的距离,确定所述子图像在所述坐标系统内的参数。 The method according to one of the preceding claims, characterized in that the sub-images obtained by the steps comprising: identifying at least two points represents the areas between the fingers; determining coordinate system having a first and second shafts , wherein the two points are located on said first shaft and said second shaft and equidistant; and using the distance between the two points, determining the parameters within the sub-image coordinate system .
  6. 6.根据权利要求5所述的方法,其特征在于所述子图像的所述参数包括所述坐标系统中、以(0.25D,0.5D)、(1.25D,0.5D)、(0.25D,-0.5D)和(1.25D,-0.5D)表示的点,其中D是所述两个点之间的距离。 6. The method according to claim 5, wherein said parameter comprises the sub-image coordinate system to (0.25D, 0.5D), (1.25D, 0.5D), (0.25D, 0.5D) and the point (1.25D, -0.5D) represented by, where D is the distance between the two points.
  7. 7.根据权利要求5或6所述的方法,其特征在于还包括对所述子图像进行规一化的步骤。 7. The method according to claim 5 or 6, characterized by further comprising the step of normalization of the sub-image.
  8. 8.根据前述权利要求之一所述的方法,其特征在于分析所述子图像包括利用伽柏滤波器。 8. The method according to one of the preceding claims, characterized in that said sub-image analysis comprises using Gabor filters.
  9. 9.根据前述权利要求之一所述的方法,其特征在于分析所述子图像包括利用伽柏分析以低分辨率分割所述子图像的层。 9. The method according to one of the preceding claims, characterized in that said sub-image analysis using Gabor analysis comprises dividing the low-resolution image of the sub-layer.
  10. 11.根据前述权利要求之一所述的方法,其特征在于将所述子图像分割为两个部分,实部和虚部,将每一部分存储为向量。 11. The method according to one of the preceding claims, characterized in that the sub-images is divided into two parts, the real and imaginary parts, each part will be stored as a vector.
  11. 12.根据权利要求11所述的方法,其特征在于将所述纹理数据与数据库中的参考信息进行比较基于以下形式的汉明距离:D0=Σi=1NΣj=1NPM(i,j)∩QM(i,j)((PR(i,j)⊗QR(i,j)+PI(i,j)⊗QI(i,j)))2Σi=1NΣj=1NPM(i,j)∩QM(i,j),]]>其中PR(QR)和PI(QI)是所述实部和所述虚部。 12. The method according to claim, characterized in that said texture data is Hamming distance based on a comparison of the form with the reference information in a database: D0 = & Sigma; i = 1N & Sigma; j = 1NPM (i, j ) & cap; QM (i, j) ((PR (i, j) & CircleTimes; QR (i, j) + PI (i, j) & CircleTimes; QI (i, j))) 2 & Sigma; i = 1N & Sigma; j = 1NPM (i, j) & cap; QM (i, j),]]> where PR (the QR) and PI (QI) is the real part and the imaginary part.
  12. 13.一种掌纹图像捕获设备,包括:外壳,其中具有窗口;设置在所述外壳中的图像传感器,用于通过窗口来捕获图像;光源,用于照亮所述窗口;以及与所述窗口相邻的至少一个凸起,其中设置所述凸起,以使其位于适当放置在窗口上的手的已知相邻位置,以便捕获包括手的手掌区域的图像。 A palmprint image capturing apparatus, comprising: a housing having a window; an image sensor disposed in the housing for capturing an image through the window; a light source for illuminating said window; and the adjacent the window at least one projection, wherein the projection is provided so as to be placed in the proper window location adjacent to a known hand, in order to capture an image including the hand palm area.
  13. 14.根据权利要求13所述的设备,其特征在于所述凸起是设置在适当放置在窗口上的手的两个或多个手指之间的栓或销。 14. The apparatus according to claim 13, characterized in that the projection is disposed between bolt or pin is placed in the appropriate window on the hand of two or more fingers.
  14. 15.根据权利要求13或14所述的方法,其特征在于所述光源是所述图像传感器位于其中心的环面。 15. The method of claim 13 or claim 14, wherein the light source is an annulus of the image sensor at the center thereof.
  15. 16.根据权利要求13到15之一所述的方法,其特征在于所述图像传感器是电荷耦合器件或互补金属氧化物半导体传感器。 13 16. The method according to one of claims 15, wherein said image sensor is a charge coupled device or a complementary metal oxide semiconductor sensor.
  16. 17.一种掌纹图像捕获设备,包括:外壳,其中具有窗口;设置在所述外壳中的图像传感器,用于通过窗口来捕获图像;光源,用于照亮所述窗口;控制器,用于控制所述图像传感器和光源,以便捕获图像;以及与所述窗口相邻的至少一个凸起,其中设置所述凸起,以使其位于适当放置在窗口上的手的已知相邻位置,以便捕获包括手的手掌区域的图像。 17. A palmprint image capturing apparatus, comprising: a housing having a window; an image sensor disposed in the housing for capturing an image through the window; a light source for illuminating said window; controller, with controlling said image sensor and a light source, so as to capture an image; and a window adjacent to said at least one projection, wherein the projection is provided so as to be placed in the proper window location adjacent the hand known to capture an image of the palm region comprises a hand.
  17. 18.根据权利要求17所述的设备,其特征在于所述凸起是设置在适当放置在窗口上的手的两个或多个手指之间的栓或销。 18. The apparatus according to claim 17, characterized in that the projection is disposed between the two bolt or pin on the proper placement of the window one or more fingers of the hand.
  18. 19.根据权利要求17或18所述的方法,其特征在于所述光源是所述图像传感器位于其中心的环面。 19. The method of claim 17 or claim 18, wherein the light source is an annulus of the image sensor at the center thereof.
  19. 20.根据权利要求17到19之一所述的方法,其特征在于所述图像传感器是电荷耦合器件或互补金属氧化物半导体传感器。 17 20. The method according to one of claim 19, wherein said image sensor is a charge coupled device or a complementary metal oxide semiconductor sensor.
CN 03822593 2002-09-25 2003-09-25 Method and apparatus for identifying a palmprint image CN100380391C (en)

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US10253914 US7466846B2 (en) 2002-09-25 2002-09-25 Method for analyzing a palm print for the identification of an individual using gabor analysis

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