利用贪心算法进行三角剖分的指纹匹配方法

针对指纹识别过程中的指纹匹配算法的准确性以及识别效果等问题,结合计算几何中平面点集的三角剖分方法-贪心算法的特点,将其引入指纹匹配处理,提出了一种基于三角网格(用贪心算法进行平面点集的三角剖分)的指纹匹配方法。该方法通过对细节点的拓扑结构进行三角划分,把空间上位置相近的细节点按照一定的规则相连,得到三角形网格,并基于该网格寻找若干参考点对,最后通过将两幅指纹图像进行姿势调整构成待识别指纹图像,使用获得的参考点对实现基于点模式的指纹匹配。经分析该方法是一种行之有效的指纹匹配方法,确保了指纹旋转、指纹平移参数计算结果的准确性,有效地提高了最终的识别效果。     

基于Delaunay三角剖分的印鉴识别算法

针对印鉴图像姿势纠正及印鉴匹配处理,引入计算几何中平面点集的三角剖分方法--Delaunay三角剖分方法和基于此的多边形三角剖分方法,并提出一种基于DT网格的印鉴识别方法.通过对两种细节点(基于线条的细节点和基于多边形的细节点)的拓扑结构进行DT三角划分,把空间上位置相近的细节点按照一定的规则相连,得到DT三角形网格,并基于该网格寻找若干参考点对,根据获得的参考点对将两幅印鉴图像进行姿势调整,使用获得的参考点对实现基于点模式的印鉴匹配.经分析该方法可以获得较多的参考点,确保了印鉴旋转、印鉴平移参数计算结果的准确性,有效地提高了最终的识别效果.     

基于Delaunay与多边形三角剖分的印鉴识别算法

研究印鉴图像姿势纠正及印鉴匹配处理问题.在研究Delaunay三角剖分方法与多边形三角剖分方法的基础上,提出一种基于DT网格的印鉴识别方法.该方法通过对两种细节点(基于线条的细节点和基于多边形的细节点)的拓扑结构进行DT三角划分.用Delaunay三角剖分方法对基于线条的细节点集进行三角剖分,对基于多边形的细节点直接进行多边形三角剖分.通过对两种细节点的拓扑结构进行三角划分,把空间上位置相近的细节点按照三角剖分的规则相连,得到DT三角形网格.然后基于该网格寻找若干参考点对,并根据获得的参考点对将两幅印鉴图像进行姿势调整.实验结果表明该方法可以获得较多的参考点,确保印鉴旋转、印鉴平移等参数计算结果的准确性,有效提高最终的识别效果.     

基于扇区划分的细节点采样指纹匹配算法

在对指纹匹配算法进行了深入研究及总结现有算法优缺点的基础上,提出了新的基于扇区划分的细节点采样指纹匹配算法。本算法以指纹图像参考点为中心,分扇区对细节点数目进行采样统计以构造新的指纹特征向量,再利用距离最小准则进行指纹匹配。实验结果表明,本算法有效提高了指纹图像的匹配效果和运行速度。     

基于贪心算法进行三角剖分的印鉴匹配方法

将计算几何中平面点集的三角剖分方法-贪心算法引入印鉴识别,研究了一种基于三角网格的印鉴匹配方法.通过对细节点的拓扑结构进行三角划分,把空间上位置相近的细节点按照一定的规则相连,得到三角形网格.然后基于该网格寻找若干参考点对,并根据获得的参考点对将两幅印鉴图像进行姿势调整.最后使用获得的参考点对实现基于点模式的印鉴匹配,经分析该方法是一种行之有效的印鉴匹配方法.     

基于贪心算法与多边形剖分的印鉴匹配算法

将计算几何中平面点集的三角剖分方法之一贪心算法与多边形三角剖分方法引入印鉴匹配,研究了一种基于三角网格(用贪心算法进行平面点集的三角剖分)的印鉴匹配方法.用贪心算法对基于线条的细节点集进行三角剖分,而对于基于多边形的细节点直接进行多边形的三角剖分.通过对两种细节点(基于线条的细节点和基于多边形的细节点)的拓扑结构进行三角划分,把空间上位置相近的细节点按照一定的规则相连,得到三角形网格.然后基于该网格寻找若干参考点对,并根据获得的参考点对将两幅印鉴图像进行姿势调整.最后使用获得的参考点时实现基于点模式的印鉴匹配,经分析该方法是一种行之有效的印鉴识别方法.     

一种基于采集设备无关性的指纹匹配算法

提出一种基于采集设备无关性的匹配算法。算法采用基于细节点集的DT网格（Delaunay三角网格）的局部结构,将源自不同指纹采集器的指纹模式统一到同一比例。根据不同的图像采集技术,选用三款采集器建立三个指纹图像库,在图像库上进行交叉匹配的结果证明了算法的有效性。     

模糊几何和纹理特征在指纹细节点后处理中的应用

指纹识别一般基于指纹细节点匹配，当指纹图像质量较差时，细节点的可靠提取十分困难，通常会产生大量的虚假细节点．为提高细节点的精度，给出一种在原始灰度指纹图像上进行细节点后处理验证的方法．在每个自动提取出的细节点上取其在原始灰度指纹图像上的局部邻域，分析邻域图像的模糊几何特征和纹理特征，然后用MLP神经网络对提取出的局部邻域特征进行分类，实现细节点类型验证．实验结果表明：文中方法能有效地去除大量的虚假细节点，与其他方法相比具有较高精度．     

基于矢量三角形的快速指纹细节匹配算法

指纹匹配是自动指纹识别系统(AFIS) 中的重要环节。该文提出了一种基于指纹纹线结构和矢量三角形相结合确定参考点，再通过极坐标进行细节匹配的指纹匹配算法。该方法将匹配分成两步，减少了拒判时间。引进了纹线信息，降低了误识率。采用矢量三角形及极坐标进行细节匹配，具有旋转和平移不变性。实验结果表明，该方法识别速度快，正确识别率高，可满足实时系统需求。     

一种新的形变指纹匹配方法

提出了一种基于细节点局部配准的形变指纹匹配方法。首先,结合细节点的纹理信息以及结构信息获取多个参照点;然后依据选取的多参照点实现模板指纹图像与输入指纹图像的全局配准从而获得指纹之间的公共区域;将公共区域内的细节点与它们最近的参照点聚类组合,形成多个分组,并将各分组内的细节点以对应的参照点为极点转化到极坐标系下建立指纹的局部对应性;最后,采用界限盒约束条件实现指纹匹配。实验结果表明,基于局部配准的指纹匹配方法对形变指纹匹配具有较好的鲁棒性,能较大提升指纹的识别性能。     

Fingerprint matching based on global alignment of multiple reference minutiae

Fingerprint matching is an important problem in fingerprint identification. A set of minutiae is usually used to represent a fingerprint. Most existing fingerprint identification systems match two fingerprints using minutiae-based method. Typically, they choose a reference minutia from the template fingerprint and the query fingerprint, respectively. When matching the two sets of minutiae, the template and the query, firstly reference minutiae pair is aligned coordinately and directionally, and secondly the matching score of the rest minutiae is evaluated. This method guarantees satisfactory alignments of regions adjacent to the reference minutiae. However, the alignments of regions far away from the reference minutiae are usually not so satisfactory. In this paper, we propose a minutia matching method based on global alignment of multiple pairs of reference minutiae. These reference minutiae are commonly distributed in various fingerprint regions. When matching, these pairs of reference minutiae are to be globally aligned, and those region pairs far away from the original reference minutiae will be aligned more satisfactorily. Experiment shows that this method leads to improvement in system identification performance.     

快速的多级指纹混合匹配方法

提出一种快速的多级指纹混合匹配方法.该方法在提取指纹图像特征,建立指纹细节点匹配模板后,对指纹进行多级匹配.首先,计算两幅指纹图像特征矢量之间的欧式距离,通过比较距离大小实现指纹的初级比对.然后利用输入指纹与库指纹的细节点模板进行二次匹配.最后,对二次匹配过程中匹配的细节点对加以分析,选取可靠的细节点对作为多参照对应点进行再次匹配.该方法融合指纹的图像特征和细节点特征,避免建立指纹细节点对应性耗时的搜索过程,同时多参照点的引入可以更好地校准细节点集.实验证明该方法快速有效.     

一种新的指纹节点匹配方法

指纹匹配是指纹识别中的一个重要问题,在指纹识别系统中常用一组节点来表示一个指纹。目前的指纹识别系统主要采用基于节点的匹配方法。文章提出了一种新的指纹节点匹配方法,在抽取节点特征时不但提取其坐标和方向,而且还提取每个节点沿着其方向和反方向的一定距离内的局部方向,在匹配两个节点时匹配这些方向信息,并将节点按方向信息排序,以加速查找。匹配时首先确定最佳参考节点,然后根据最佳参考节点下匹配节点对的位置和方向差异再次旋转和平移输入指纹,最后在直角坐标下再次匹配两个指纹。实验表明,该方法能有效地提高系统的识别性能。     

A deformable model for fingerprint matching

The process of automatic fingerprint matching is affected by the nonlinear deformation introduced in the image during fingerprint sensing. Given several template impressions of a finger, we estimate the “average” deformation of each template impression by comparing it with the rest of the impressions of that finger. The average deformation is developed using the thin plate spline (TPS) model and is based on minutia point correspondences between pairs of fingerprint impressions. The estimated average deformation is utilized to pre-distort the minutiae points in the template image before matching it with the minutiae points in the query image. We show that the use of an average deformation model leads to a better alignment between the template and query minutiae points. An index of deformation is proposed for choosing the deformation model with the least variability arising from a set of template impressions corresponding to a finger. Our experimental data consists of 1600 fingerprints corresponding to 50 different fingers collected over a period of 2 weeks. It is shown that the average deformation model leads to an improvement in the alignment between impressions originating from the same finger.     

Fingerprint reconstruction: from minutiae to phase

Fingerprint matching systems generally use four types of representation schemes: grayscale image, phase image, skeleton image, and minutiae, among which minutiae-based representation is the most widely adopted one. The compactness of minutiae representation has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original grayscale fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. These techniques try to either reconstruct the skeleton image, which is then converted into the grayscale image, or reconstruct the grayscale image directly from the minutiae template. However, they have a common drawback: Many spurious minutiae not included in the original minutiae template are generated in the reconstructed image. Moreover, some of these reconstruction techniques can only generate a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed to reconstruct the phase image, which is then converted into the grayscale image. The proposed reconstruction algorithm not only gives the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. Specifically, a fingerprint image is represented as a phase image which consists of the continuous phase and the spiral phase (which corresponds to minutiae). An algorithm is proposed to reconstruct the continuous phase from minutiae. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against different impressions of the original fingerprint) using a commercial fingerprint recognition system. Given the reconstructed image from our algorithm, we show that both types of attacks can be successfully launched against a fingerprint recognition system.     

OPTIMIZING THE ROTATION AND TRANSLATION OF FINGERPRINT IMAGES USING GENETIC ALGORITHM

In the minutiae-based fingerprint authentication system, the minutiae in the query image are required to be matched with the minutiae of the reference image that is stored in the database. Ideally, the minutiae extracted from the different impressions of the same fingerprint must match with each other, but practically, because of displacement, rotation, and other linear/ nonlinear distortions, minutiae extracted from different impressions of the same fingerprint do not match with each other. In order to maximize the number of matching minutiae, the alignment of the two fingerprints is required. Correctly aligning the fingerprints requires the translation and rotation to be recovered exactly. In this article, a new genetic-algorithm (GA)-based relative alignment algorithm for the alignment of reference and query fingerprint images is proposed. With the proposed algorithm there is no need to find the reference core or delta point because reliable detection of these reference points is a difficult task. In the proposed algorithm, all the three parameters x, y (translation), and θ (rotational) have been optimized separately. In order to improve the processing time, two acceleration steps have also been implemented. The experiments conducted on the FVC2002/Db1_a database reveal that a high accuracy has been achieved with the proposed method.     

一种新的指纹匹配方法

针对基于点模式匹配的指纹匹配算法速度较慢的现状，设计了一种新的指纹匹配方法，即利用纹线匹配技术来寻找基准点对的指纹匹配算法．该算法首先基于指纹纹线的相似程度寻找一对基准特征点；然后根据基准点对的坐标，计算两幅指纹图象(模板图象、待识图象)的相对平移和旋转参数，并将待识图象相对于模板图象进行图象姿势纠正；最后使用坐标匹配的方法统计两幅图象能够匹配的特征点数目．以实现两枚指纹的匹配．实验证明．该算法匹配速度很快，误识率低，准确性高，并具有图象旋转平移不变性．对面积适中的指纹图象，匹配结果可以满足在线应用的需要．该算法有望发展成为一种实用、有效的指纹匹配技术．     

基于指纹细节点的可撤销比特串模板生成算法

为了提高指纹模板算法的安全性能,设计了一种基于细节点的可撤销比特串指纹模板生成算法。首先对指纹图像进行预处理,提取指纹的细节点特征,然后对细节点特征进行量化和映射生成比特串模板,最后结合用户PIN码生成可撤销指纹模板。在指纹数据库FVC2002-DB1和DB2上的实验表明,该算法具有更好的安全性和认证性能,满足可撤销性、多样性和不可逆性。