Fingerprint image reconstruction from standard templates

A minutiae-based template is a very compact representation of a fingerprint image and for a long time it has been assumed that it did not contain enough information to allow the reconstruction of the original fingerprint. This work proposes a novel approach to reconstruct fingerprint images from standard templates and investigates to what extent the reconstructed images are similar to the original ones (i.e., those the templates were extracted from). The efficacy of the reconstruction technique has been assessed by estimating the success chances of a masquerade attack against nine different fingerprint recognition algorithms. The experimental results show that the reconstructed images are very realistic and that, although it is unlikely they can fool a human expert, there is a high chance to deceive state-of-the-art commercial fingerprint recognition systems.     

基于Delaunay三角化的指纹匹配方法

将计算几何的三角划分方法引入指纹匹配，研究了一种基于DT（Delaunay triangulation）网的指纹匹配方法．通过对细节点的拓扑结构进行DT划分，把空间上位置相近的细节点按照一定规则相连，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指纹图像进行姿势调整．最后使用获得的参考点对实现基于点模式的指纹匹配．算法在第1届中国生物特征识别竞赛指纹组的测试结果证明了有效性．     

Preprocessing and postprocessing for skeleton-based fingerprint minutiae extraction

In this paper, we propose to use the fingerprint valley instead of ridge for the binarization-thinning process to extract fingerprint minutiae. We first use several preprocessing steps on the binary image in order to eliminate the spurious lakes and dots, and to reduce the spurious islands, bridges, and spurs in the skeleton image. By removing all the bug pixels introduced at the thinning stage, our algorithm can detect a maximum number of minutiae from the fingerprint skeleton using the Rutovitz Crossing Number. This allows the true minutiae preserved and false minutiae removed in later postprocessing stages. Finally, using the intrinsic duality property of fingerprint image we develop several postprocessing techniques to efficiently remove spurious minutiae. Especially, we define an H-point structure to remove several types of spurious minutiae including bridge, triangle, ladder, and wrinkle all together. Experimental results clearly demonstrate the effectiveness of the new algorithms.     

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。     

基于细节点投影的可撤销指纹模板生成算法

为了改善指纹模板保护算法的可撤销性、不可逆性等性能, 设计了一种基于细节点投影的可撤销指纹模板生成算法.首先对指纹图像进行预处理, 提取指纹的细节点特征, 并筛选出采样半径范围内的有效细节点, 然后对细节点进行直线投影, 将投影后的向量映射到二维网格, 生成固定长度的一维比特串, 再结合用户PIN码生成可撤销指纹模板.在指纹数据库FVC2002-DB1和DB2上的实验结果表明, 该算法不仅提高了指纹模板认证的稳定性, 而且在可撤销性、不可逆性和安全性等方面均具有较好性能.     

From template to image: reconstructing fingerprints from minutiae points

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line integral convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint     

基于二值图像的指纹细节点提取

指纹细节点提取是自动指纹识别研究的关键问题之一,尽管已有很多基于细化图像分析的方法和基于原始灰度图像分析的方法,但该问题还没有得到很好的解决。因此提出了一种从二值图像上提取细节点的方法。首先通过行程匹配得到二值指纹图像中的图段,然后根据图段的结构形式和图段之间的连接关系进行细节点的判定,细节点的方向通过直线试探法获得。实验结果表明：与基于细化图像的方法相比不仅具有较高的效率,而且能使指纹匹配达到较高的精度。     

基于MBF200的嵌入式系统指纹预处理算法研究

随着指纹识别技术的不断发展以及嵌入式系统应用的日益广泛,人们对于如何将指纹识别技术用于嵌入式系统的研究也越来越多。文中针对MBF200采集到的指纹图像的特点,讨论了一种用于嵌入式系统的指纹图像预处理算法。介绍了嵌入式指纹识别系统的硬件平台,再依据由该系统采集到的指纹图像的特点,提出通过纹线跟踪来增强指纹图像,并对现有的细化后纹线跟踪去伪特征算法作了改进,调整了去伪顺序。实验表明,该算法具有快速处理的良好效果,满足嵌入式系统的要求。     

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．     

Learning fingerprint minutiae location and type

For simplicity of pattern recognition system design, a sequential approach consisting of sensing, feature extraction and classification/matching is conventionally adopted, where each stage transforms its input relatively independently. In practice, the interaction between these modules is limited. Some of the errors in this end-to-end sequential processing can be eliminated, especially for the feature extraction stage, by revisiting the input pattern. We propose a feedforward of the original grayscale image data to a feature (minutiae) verification stage in the context of a minutiae-based fingerprint verification system. This minutiae verification stage is based on reexamining the grayscale profile in a detected minutia's spatial neighborhood in the sensed image. We also show that a feature refinement (minutiae classification) stage that assigns one of two class labels to each detected minutia (ridge ending and ridge bifurcation) can improve the matching accuracy by ∼1% and when combined with the proposed minutiae verification stage, the matching accuracy can be improved by ∼3.2% on our fingerprint database.     

基于纹理结构的指纹匹配算法

为克服传统细节点匹配模型的不足,对指纹的纹理结构进行了分析,利用指纹纹线的不同结构作为指纹图像的特征。提出了一套基于指纹纹线轮廓的特征提取和匹配算法,并且采用了分步匹配,减少了拒判时间,该算法具有平移、旋转不变性。由于利用了指纹的结构信息,对低质量指纹图像有一定的适应度。实验结果表明,该算法具有相当高的识别率和较强的鲁棒性。     

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.     

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.     

指纹图像预处理和特征提取算法的Matlab实现

在指纹识别中,作为指纹匹配的前提和基础,指纹图像的预处理和指纹图像的特征提取对整个指纹识别算法起着至关重要的作用。本文设计了一套指纹图像预处理和指纹特征提取算法,利用方向滤波的方法对原始图像进行增强,使用改进的细化算法进行细化,最后基于脊线跟踪的方法,用改进的算法更为简洁而有效地避免了各种伪特征。设计的算法经Matlab实现,实验效果良好。该算法为指纹识别进一步应用到实际提供了一定的技术基础。     

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Matlab实现的指纹图像细节特征提取

指纹图像的特征提取是指纹识别的关键,而指纹匹配通常基于细节点匹配.介绍了一套基于Matlab实现的指纹细节特征提取方法,并给出了去伪算法.指纹特征提取是从细化后的指纹图中得到细节特征点(即端点和分叉点),此特征点含有大量的伪特征,既耗时又影响匹配精度.采用了边缘去伪和距离去伪,使得特征点去伪前后减小了近1/3,然后提取可靠特征点信息,以便实现指纹匹配.实验证明,用Matlab实现的这种方法,既简单快速,而且具有较高的准确率.     

指纹图像细节点及显著特征的提取

提出了一种改进的指纹细节特征提取算法。该算法首先在细化后的指纹图像上直接提取原始细节特征点集,然后针对不同的噪声,采用针对性的算法,将各类噪声引起的伪特征点分别予以删除,最终保留下来的特征点集即视为真正的特征点集。通过分析细节特征点之间的联系,在整幅图像范围内构造了3种类型的显著特征,提出了一种显著特征提取算法,在已提取的特征点集的基础上,提取出指纹图像的显著特征。     

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。     

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．     

基于中心点的指纹细节结构匹配算法

指纹细节匹配算法是自动指纹识别系统(AFIS)中一项关键的任务,目前存在大量的研究和算法.依据算法是否依赖中心点,指纹细节点匹配算法可以分为两类:基于中心点的匹配算法和非中心点匹配算法.大多数非中心点匹配算法都非常耗时,因此不适合在线应用.而基于中心点方法的效率相对较高,但是这类算法极度依赖于中心点的定位精度.在本文中,提出了一种全新的基于中心点的指纹细节结构匹配算法,该算法综合了基于中心点匹配算法和非中心点匹配算法的优点,同时又避免了二者的缺点.首先利用中心点检测算法获得中心点的位置,然后在中心区域定义了一些局部的结构,同时利用这些局部结构寻找指纹细节的对应点,并通过对应点和中心点的相对关系来确认这些对应细节点.其次利用这些细节对应点匹配全局的细节信息,最后,利用匹配细节的全局距离和距离方差来判决最终匹配结果.实验结果表明,算法的匹配效果非常好,同时匹配效率较高,非常适合在线指纹识别系统的应用.