一种基于模板的指纹核心点定位算法*

针对传统指纹核心点定位方法的不足,提出了新的指纹核心点定位算法。该算法是一种利用核心点定位模板联合指纹核心点周围结构特征,进行综合分析判定的定位方法。将指纹核心点的定位过程分为两个阶段：第一阶段计算指纹方向图,在方向图的基础上结合核心点周围特殊的结构特征完成粗定位,确定备选点;第二阶段对备选点进行识别模板判断,甄别出真正的参考点。通过FVC2004指纹库验证该方案的有效性。实验表明,该方案对各类指纹图像都能较准确地定位核心点,计算简单、可靠性高、耗时短、鲁棒性好。     

利用遗传算法对指纹的核心点进行检测

指纹的核心点是指纹的主要特征,对指纹匹配和指纹分类具有十分重要的作用.本文对指纹核心点的位置和方向进行了新的定义,接着提出一种新颖的基于遗传算法的方法对指纹核心点进行定位以及对核心点的方向进行估计.首先根据定义给出核心点位置和方向的打分函数并根据指纹的方向域信息确定了核心点的候选区域,接着把打分函数作为遗传算法的进化适应度函数在这个核心点候选区域内利用遗传算法对指纹核心点及其方向进行快速求极值,这样可以同时得到指纹的核心点的坐标和方向.实验结果表明本文提出的方法可以对指纹的核心点的位置和方向进行精确的定位和估计.     

复合指纹核心点定位方法

对于指纹核心点定位的诸多算法,通常都存在漏检、偏检以及耗时过长等缺点.提出了一种复合指纹核心点定位算法,首先运用方向图信息进行粗定位.确定核心点的候选区域,然后通过Poincare索引法精确定位核心点.对于粗定位改进了传统算法的检测条件,定位出的核心块既精确而且面积较小,大大减少了下一步精确定位核心点的运算量,且对Poincare索引法检测中心点存在的不足,引入方向一致性度量加以弥补.经大量实验证明算法适应性较强,很好的解决了漏检等问题.     

基于脊线跟踪的指纹核心点检测

准确、可靠地检测指纹核心点对于指纹的分类和匹配有重要的意义。针对指纹图像核心点提取中准确判断和精确定位的难题,介绍了一种比较好的核心点检测算法。根据核心点是纹线曲率最大的点的定义,利用脊线跟踪的方法求出核心点。实验结果证明该方法能够从指纹图像中较精确、可靠地提取出核心点。并具有较好的鲁棒性。     

基于加权平均梯度方向场和改进Poincare Index的指纹奇异点检测算法

针对指纹图像具有局部平行性和渐变性以及邻域的脊线方向相关性高的特点,提出了一种基于加权平均梯度的指纹方向场算法。改进了传统的Poincare Index指纹奇异点检测算法。实验证明,在采用加权平均梯度算法获取的方向场上利用改进的Poincare Index算法可实现对低质量指纹图像的奇异点的准确提取。     

一种用于指纹方向场估计的网格插值模型

指纹方向场的估计是指纹识别预处理算法中的重要环节,对算法识别效率起到关键作用。本文提出了一种网格插值模型,该模型以指纹奇异点为中心,将指纹平面做网格划分,利用插值算法建立了方向场与指纹奇异点之间的非线性关系。模型中利用了指纹的全局信息来调整网格点的值,使得它与传统的基于局部信息的方向场算法有本质的区别。在FVC2002 和FVC2004 指纹数据库上的实验结果表明,该模型比传统算法具有更高的准确性和鲁棒性,同时对于低质量的指纹图像,仍然能够给出很好的方向场估计。     

基于方向场的指纹分类快速算法

本文经过分析比较以前的指纹分类的算法,提出一种基于Walsh函数的算法获取指纹的奇点和方向场,并利用方向场进行指纹分类的快速算法.根据指纹方向场的特点,以指纹奇点为中心,考察其邻近八个指定区域方向场的大致走向.依据传统的指纹分类方法将指纹分成弓型、斗型、左箕型和右箕型四种类型.实验表明,这种算法程序编制简单,计算速度快,检测结果可靠.     

基于方向一致性的指纹中心点检测算法

提出了一种指纹中心点检测以及确定其方向的算法。该算法通过分层分析指纹方向场的一致性,检测出方向场中方向一致性较低的点,然后利用poincare索引辨别检测出方向一致性低的点的类型。类型确定后,提出了一种使用以指纹中心点为圆心的环形域中的方向信息,确定指纹中心点方向的新算法。在FVC2002指纹图像库中进行实验,结果表明,该算法检测指纹中心点准确、快速,所确定的中心点方向精确、稳定,对噪声具有较高的鲁棒性,能够很好地满足指纹对齐的要求。     

一种新的指纹奇异点快速检测方法

作为指纹最重要的全局特征之一,奇异点在基于模型的方向场计算、人工合成指纹、指纹分类、指纹特征匹配等方面发挥了非常重要的作用.在指纹方向场分割的基础上提出了一种称之为方向丰富度的特征,并据此形成了一种新的指纹奇异点快速检测方法.该方法首先将指纹方向场分割为一系列互不重叠的同质区域;然后通过同质区域边缘检测及边缘端点提取实现了奇异点快速定位;最后依据奇异点处方向丰富度特性判断其类型.实验验证了文中算法的有效性.     

基于方向的指纹奇异点提取

准确、可靠地检测指纹奇异点(核心点和三角点)对于指纹的分类和匹配有重要的意义。针对指纹图像奇异点提取中准确判断和精确定位的难题,介绍了一种比较好的奇异点检测算法。根据奇异点的性质,利用Poincare Index方法求出核心点和三角点。根据相关规则,清除虚假奇异点。实验结果证明该方法能够从指纹图像中较精确、可靠地提取出奇异点。用该方法对不同质量的指纹图像进行实验,并与其他方法进行比较,结果表明该方法更加有效、可靠,具有很好的鲁棒性。     

低质量指纹图像方向场提取

指纹方向场提取是自动指纹识别系统中的重要环节,是解决自动指纹识别中的某些关键技术的基础.提出一种通过线性投影分析和在sin域中用加权低阶2维勒让德多项式拟合提取指纹方向场的方法;能够解决大噪声低质量指纹图像方向场难以准确提取的问题;在FVC 2000指纹数据库中的大量实验结果表明,与已有基于梯度的指纹方向场估计算法相比,本文方法有更好的提取精度和鲁棒性,对于大噪声的低质量指纹图像能给出很好的方向场估计.     

低质量指纹图像方向场提取方法

指纹方向场提取是自动指纹识别系统中的重要环节,是解决自动指纹识别中的某些关键技术的基础。提出了一种通过线性投影分析和在sin域中用加权低阶二维勒让德多项式拟合提取指纹方向场的方法;解决了大噪声低质量指纹图像方向场难以准确提取的问题;在FVC 2000指纹数据库中的大量实验表明,与已有基于梯度的指纹方向场估计算法相比,该方法有更好的提取精度和鲁棒性,对于大噪声的低质量指纹图像能给出很好的方向场估计。     

Computing oriented texture fields

The first step is the analysis of oriented texture consists of the extraction of an orientation field. The orientation field is comprised of the angle and coherence images, which describe at each point the dominant local orientation and degree of anisotropy, respectively. A new algorithm for computing the orientation field for a flow-like texture is presented. The basic idea behind the algorithm is to use an oriented filter, namely the gradient of Gaussian, and perform manipulations on the resulting gradient vector field. The most important aspect of the new algorithm is that it is provably optimal in estimating the local orientation of an oriented texture. An added strength of the algorithm is that it is simpler and has a better signal-to-noise ratio than previous approaches, because it employs fewer derivative operations. We also propose a new measure of coherence, which works better than previous measures. The estimates for orientation and coherence are related to measures in the statistical theory of directional data. We advocate the use of the angle and coherence images as intrinsic images. An analysis of oriented textures will require the computation of these intrinsic images as a first step. In this sense, the computation of the orientation field, resulting in the intrinsic images, is indispensible in the analysis of oriented textures. We provide results from several experiments to indicate the usefulness of the angle and coherence intrinsic images. These results show that the notion of scale plays an important role in the interpretation of textures. Further, measures defined on these intrinsic images are useful for the inspection of surfaces.     

Fingerprint ridge orientation field reconstruction using the best quadratic approximation by orthogonal polynomials in two discrete variables

This paper proposes a novel algorithm for reconstructing the fingerprint orientation field (FOF). The basic idea of the algorithm is to reconstruct the ridge orientation by using the best quadratic approximation by orthogonal polynomials in two discrete variables. We first estimate the local region orientation by the linear projection analysis (LPA) based on the vector set of point gradients, and then reconstruct the ridge orientation field using the best quadratic approximation by orthogonal polynomials in two discrete variables in the sine domain. In this way, we solve the problem that is difficult to accurately extract low quality fingerprint image orientation fields. The experiments with the database of FVC 2004 show that, compared to the state-of-the-art fingerprint orientation estimation algorithms, the proposed method is more accurate and more robust against noise, and is able to better estimate the FOF of low quality fingerprint images with large areas of noise.     

A Novel Algorithm for Detecting Singular Points from Fingerprint Images

Fingerprint analysis is typically based on the location and pattern of detected singular points in the images. These singular points (cores and deltas) not only represent the characteristics of local ridge patterns but also determine the topological structure (i.e., fingerprint type) and largely influence the orientation field. In this paper, we propose a novel algorithm for singular points detection. After an initial detection using the conventional Poincaré Index method, a so-called DORIC feature is used to remove spurious singular points. Then, the optimal combination of singular points is selected to minimize the difference between the original orientation field and the model-based orientation field reconstructed using the singular points. A core-delta relation is used as a global constraint for the final selection of singular points. Experimental results show that our algorithm is accurate and robust, giving better results than competing approaches. The proposed detection algorithm can also be used for more general 2D oriented patterns, such as fluid flow motion, and so forth.     

基于奇异点的字典模型指纹方向场去噪方法*

方向场估计是指纹识别过程中非常重要的步骤。传统方法如基于梯度的方法等在处理潜指纹图像时很容易受噪音干扰,而最近提出的基于字典模型的方法无法解决“真词错误”的问题。针对上述问题,本文提出一种融合了零极点模型的字典模型的指纹方向场去噪方法,即将指纹方向场看做是零极点控制的方向场和平滑的残差方向场相叠加的结果,通过首先用零极点模型生成正确的零极点控制的方向场,然后用字典模型修正残差方向场方向场,最后将零极点模型生成的方向场与去噪后的残差方向场融合形成重建方向场,通过基于奇异点的字典模型,我们解决了“真词错误”的问题。为了验证算法的有效性,在NIST SD27潜指纹图像数据库上进行了实验。实验结果表明：对于潜指纹,本文算法能获得比字典模型更精确的方向场,继而可以更好地增强潜指纹图像,并在后续的匹配实验中取得更好的结果。     

Singular points detection based on zero-pole model in fingerprint images

An algorithm is proposed which combines Zero-pole Model and Hough Transform(HT) to detect singular points. Orientation of singular points is defined on basis of Zero-pole Model which can further explain the practicability of Zero-pole Model. Contrary to orientation field generation, detection of singular points is simplified to determine the parameters of Zero-pole Model. HT uses rather global information of fingerprint images to detect singular points. This makes our algorithm more robust to noise than methods which only use local information. As Zero-pole Model may have a little warp from actual fingerprint orientation field, Poincare index is used to make position adjustment in neighborhood of the detected candidate singular points. Experimental results show that our algorithm performs well and fast enough for real time application in database NIST-4.     

Critical point detection in fluid flow images using dynamical system properties

This paper introduces an algorithm for critical point detection in textured fluid flow images. A new measure is defined, based on dynamical system properties, that identifies candidate critical points in an orientation field. The candidates are verified or rejected based on estimates of the local flow field properties. The algorithm can locate partially occluded and degraded flow structures, and applications of this algorithm to experimental flow imagery are included. The algorithm performance is quantified, and it is compared to other detectors.     

Orientation feature for fingerprint matching

This paper describes a fingerprint verification algorithm based on the orientation field. The orientation field of a fingerprint image has also been used for image alignment. Area around the core point has been employed as an area of interest for determining the orientation feature map. The algorithm has been tested on two databases (database available from University of Bologna, Biometrics Laboratory and FVC2002). The performance of the algorithm is measured in terms of receiver operating characteristics (ROC). For the University of Bologna database, at ∼0% false acceptance rate (FAR) the genuine acceptance rate (GAR) observed is ∼78% and at ∼11% FAR, GAR is ∼97%. For the FVC2002 database at ∼0% FAR the GAR observed is 75% and at ∼18% FAR, GAR is 93%. Proposed algorithm yields better GAR at low FAR with reduced computational complexity. Because of simplicity in computations the algorithm can be easily implemented as an embedded automatic fingerprint identification system (AFIS).