一种扭曲指纹图像的细节点修正方法

非线性形变在指纹图像中非常普遍,严重时会导致指纹识别系统的识别性能显著降低,是指纹图像识别领域重要的研究课题之一.尤其对基于细节点的指纹匹配方法而言,由于相当一部分细节点的位置和方向都在非线性形变的作用下发生了改变,从而导致算法匹配准确率下降.在对非线性形变图像反复观察和实验的基础上,发现了细节点偏离的一些有用的规律,并用于细节点的位置和方向调整.提出了一种基于多参考细节点对齐修正指纹图像中部分细节点的方法,首先获取一组匹配细节点对作为基准点对,然后再以此为基础对邻域其他细节点进行修正,在FVC2004 DB2数据集上进行了一系列实验,实验结果表明该算法能够明显改善匹配性能.     

基于图的指纹匹配算法

研究了基于端点和分支点的点模式指纹匹配算法，通过对端点和分支点的完全图进行匹配，使其具有较好的稳定性。对匹配图像端点和分支点进行分组，通过每组的点和模板图像的细节点进行点模式匹配来实现指纹匹配，有效地降低了运算复杂度。实验结果表明，该方法在指纹产生非线性形变、指纹图像质量低等情况下有较好的性能，具有较强的实用性。     

一种基于结构匹配的指纹匹配算法

提出了一种基于结构匹配的指纹匹配算法,不仅考虑了指纹的全局特性而且利用了指纹局部结构的平移和旋转不变性。该算法可克服在采集指纹图像时由于非线性形变所引起的细节点的位置、细节点的方向及指纹图像中细节点之间距离改变产生的影响,降低了匹配算法的拒识率。试验结果表明所提出的方法确实提高了指纹匹配算法的有效性。     

一种基于基准点的指纹匹配算法

在图像中心区域为若干细节点分别构造局部结构,根据这些结构的匹配结果实现了基准点的精确定位。在指纹匹配阶段引入一个新的属性,即细节点到基准点之间的纹线切割数目,该属性对指纹的平移和旋转以及非线性形变具有不变性。指纹匹配时对该属性进行比较,排除明显不匹配的细节点,根据可变大小界限盒的方法对细节点进一步匹配。实验结果表明,该算法效率比原有算法提高了10%～20%,且实现了较高的匹配精度。     

基于细节点邻域信息的可撤销指纹模板生成算法

为了提高指纹模板算法的安全性等性能,设计了一种基于细节点邻域信息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,然后采用改进的细节点描述子采样结构提取细节点邻域的纹线特征,最后结合用户PIN码生成指纹模板,同时结合贪婪算法设计了相应的指纹匹配算法.在指纹数据库FVC2002-DB1和DB2上的实验表明,该算法具有良好的认证性能,能较好地满足可撤销性、多样性和不可逆性,而且改进的采样结构在没有降低系统识别性能的情况下,进一步拓展了细节点描述子的采样结构方式.     

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.     

融合图像显著性与特征点匹配的形变目标跟踪

目的 针对目标在跟踪过程中出现剧烈形变,特别是剧烈尺度变化的而导致跟踪失败情况,提出融合图像显著性与特征点匹配的目标跟踪算法。方法 首先利用改进的BRISK（binary robust invariant scalable keypoints）特征点检测算法,对视频序列中的初始帧提取特征点,确定跟踪算法中的目标模板和目标模板特征点集合;接着对当前帧进行特征点检测,并与目标模板特征点集合利用FLANN（fast approximate nearest neighbor search library）方法进行匹配得到匹配特征点子集;然后融合匹配特征点和光流特征点确定可靠特征点集;再后基于可靠特征点集和目标模板特征点集计算单应性变换矩阵粗确定目标跟踪框,继而基于LC（local contrast）图像显著性精确定目标跟踪框;最后融合图像显著性和可靠特征点自适应确定目标跟踪框。当连续三帧目标发生剧烈形变时,更新目标模板和目标模板特征点集。结果 为了验证算法性能,在OTB2013数据集中挑选出具有形变特性的8个视频序列,共2214帧图像作为实验数据集。在重合度实验中,本文算法能够达到0.567 1的平均重合度,优于当前先进的跟踪算法;在重合度成功率实验中,本文算法也比当前先进的跟踪算法具有更好的跟踪效果。最后利用Vega Prime仿真了无人机快速抵近飞行下目标出现剧烈形变的航拍视频序列,序列中目标的最大形变量超过14,帧间最大形变量达到1.72,实验表明本文算法在该视频序列上具有更好的跟踪效果。本文算法具有较好的实时性,平均帧率48.6帧/s。结论 本文算法能够实时准确的跟踪剧烈形变的目标,特别是剧烈尺度变化的目标。     

利用旋转模板匹配方法对SIFT算法的改进

针对SIFT算法经常出现误匹配的情况,提出一种匹配算法.该方法在SIFT算法之后嵌入一种旋转模板匹配算法.利用特征点方向,对图像进行旋转,使用旋转后的特征点邻域进行模板匹配,对SIFT算法的结果进行二次筛选.实验证明,该方法能够有效地提高SIFT算法匹配正确率,实现对SIFT算法的改良.     

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。     

指纹快速匹配算法研究

设计了指纹快速匹配算法。算法以分类时获取的奇异点为参考点对指纹进行定位,利用奇异点特殊邻近结构缩小细节点匹配规模,在可变界限盒的基础上设计合理匹配策略。该方法不仅可以提高计算速度,也能够适应指纹非线性形变的特点,增加算法鲁棒性。在FVC2000数据库上的测试结果显示,该算法在保证自动指纹识别系统识别准确性的同时,还使得指纹辨识速度有了显著的提高。     

指纹特征规模研究

自动指纹识别系统是通过对指纹的特征(节点)进行匹配对比来实现识别认证的,识别的效果主要取决于指纹输入设备、特征提取和匹配算法.为了研究指纹输入设备和特征提取算法对识别精度的影响,在已知指纹特征位置分布的情况下,建立了指纹特征匹配的数学模型,通过计算两枚指纹匹配的概率,引入了指纹特征规模的概念,它是对指纹输入设备和特征提取算法的一个综合评价.最终得到结论:若节点匹配的数目不变,可以通过降低指纹的特征规模,来提高识别精度;若系统的特征规模不变,可以通过增加节点匹配的数目,来提高系统识别精度.     

一种采用脊线特征的指纹模糊匹配方法

针对目前指纹识别系统主要采用手指上细节点的分布来表征和匹配指纹,提出了一种采用指纹脊线特征的匹配算法,以提高细节点数量较少情况下的匹配精度.在特征提取阶段,通过脊线采样,只存储脊线采样点集以降低存储量;在匹配时,对欲匹配的两指纹利用细节特征配准脊线集,在重合区域内对两指纹脊线统一进行编码,通过编码的比较确定相似脊线;以相似脊线的相同位置编码为论域,以相同位置编码的相似程度为隶属度,建立衡量脊线相似程度的模糊集,采用加权平均法对多个相似脊线模糊集进行综合评判得到两指纹脊线总体相似度.最后将脊线匹配相似度与细节点匹配相似度进行加权融合得到两指纹最终的相似度.在FVC2004指纹库上的实验表明该算法能够有效提高指纹匹配的准确性.     

Fingerprint matching by genetic algorithms

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint-matching approach based on genetic algorithms (GA), which tries to find the optimal transformation between two different fingerprints. In order to deal with low-quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design a fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.     

Minutiae-based template synthesis and matching for fingerprint authentication

Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various distortions introduced during the acquisition process. An effective approach to account for within-class variations is by capturing multiple enrollment impressions of a finger. The focus of this work is on effectively combining minutiae information from multiple impressions of the same finger in order to increase coverage area, restore missing minutiae, and eliminate spurious ones. We propose a new, minutiae-based, template synthesis algorithm which merges several enrollment feature sets into a “super-template”. We have performed extensive experiments and comparisons to demonstrate the effectiveness of the proposed approach using a challenging public database (i.e., FVC2000 Db1) which contains small area, low quality fingerprints.     

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.     

Fingerprint warping using ridge curve correspondences

The performance of a fingerprint matching system is affected by the nonlinear deformation introduced in the fingerprint impression during image acquisition. This nonlinear deformation causes fingerprint features such as minutiae points and ridge curves to be distorted in a complex manner. A technique is presented to estimate the nonlinear distortion in fingerprint pairs based on ridge curve correspondences. The nonlinear distortion, represented using the thin-plate spline (TPS) function, aids in the estimation of an "average" deformation model for a specific finger when several impressions of that finger are available. The estimated average deformation is then utilized to distort the template fingerprint prior to matching it with an input fingerprint. The proposed deformation model based on ridge curves leads to a better alignment of two fingerprint images compared to a deformation model based on minutiae patterns. An index of deformation is proposed for selecting the "optimal" deformation model arising from multiple impressions associated with a finger. Results based on experimental data consisting of 1,600 fingerprints corresponding to 50 different fingers collected over a period of two weeks show that incorporating the proposed deformation model results in an improvement in the matching 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.     

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.