A hybrid wavelet-based fingerprint matcher

In this work we present a hybrid fingerprint matcher system based on the multi-resolution analysis of the fingerprint pattern and on minutiae-based registration module. Two fingerprints are first aligned using their minutiae, then the images are divided in sub-windows and each sub-window is decomposed into frequency sub-bands at different decomposition levels using a set of wavelet functions, finally a distinct classifier is trained on each sub-band to distinguish matching pairs of fingerprint from non-matching one (defining a two-class matching problem). The features extracted for the matching are the standard deviation of the image convolved with 16 Gabor filters. The selection among the pool of matchers, is performed by running Sequential Forward Floating Selection. The retained matchers are weighted by a novel localized quality measure and combined by a fusion rule. Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed approach.     

On the individuality of fingerprints

Fingerprint identification is based on two basic premises: (1) persistence and (2) individuality. We address the problem of fingerprint individuality by quantifying the amount of information available in minutiae features to establish a correspondence between two fingerprint images. We derive an expression which estimates the probability of a false correspondence between minutiae-based representations from two arbitrary fingerprints belonging to different fingers. Our results show that (1) contrary to the popular belief, fingerprint matching is not infallible and leads to some false associations, (2) while there is an overwhelming amount of discriminatory information present in the fingerprints, the strength of the evidence degrades drastically with noise in the sensed fingerprint images, (3) the performance of the state-of-the-art automatic fingerprint matchers is not even close to the theoretical limit, and (4) because automatic fingerprint verification systems based on minutia use only a part of the discriminatory information present in the fingerprints, it may be desirable to explore additional complementary representations of fingerprints for automatic matching.     

Improving automated latent fingerprint identification using extended minutia types

Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.     

Latent fingerprint matching

Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects: Latent fingerprints are inadvertent impressions left by fingers on surfaces of objects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. Poor quality of ridge impressions, small finger area, and large nonlinear distortion are the main difficulties in latent fingerprint matching compared to plain or rolled fingerprint matching. We propose a system for matching latent fingerprints found at crime scenes to rolled fingerprints enrolled in law enforcement databases. In addition to minutiae, we also use extended features, including singularity, ridge quality map, ridge flow map, ridge wavelength map, and skeleton. We tested our system by matching 258 latents in the NIST SD27 database against a background database of 29,257 rolled fingerprints obtained by combining the NIST SD4, SD14, and SD27 databases. The minutiae-based baseline rank-1 identification rate of 34.9 percent was improved to 74 percent when extended features were used. In order to evaluate the relative importance of each extended feature, these features were incrementally used in the order of their cost in marking by latent experts. The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy.     

一种快速的指纹参照点对选取方法研究

快速准确地确定匹配参照点对是实现基于点模式指纹识别的一个关键问题,针对该问题本文提出了一种基于脊线校准的确定匹配参照点对的新方法。该方法首先利用细节点间的距离、类型以及细节点与脊线样点之间构成的网状结构来构建新的局部特征向量,然后在这些结构特征向量空间中搜索最为相似特征向量,确定出最优匹配参照点对。实验结果表明,本文提出的算法不仅速度快,而且准确性也有了较大提高。     

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.     

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。     

一种结合节点和方向场的指纹匹配算法

指纹匹配是指纹识别中的一个重要问题,直接影响着系统的正确率。目前普遍使用的匹配算法主要是基于节点的匹配算法。Ross提出了结合节点和纹理特征的指纹匹配方法,但Ross方法时间代价大。本文提出了结合节点和方向场的匹配算法,在用节点进行旋转和平移对齐的基础上,计算两个指纹方向场的一致性。实验结果表明：该算法可以比现现有的基于节点的匹配算法更准确地区分两个指纹,同时比Ross方法更快。     

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 novel ant colony optimization algorithm for large-distorted fingerprint matching

Large distortion may be introduced by non-orthogonal finger pressure and 3D–2D mapping during the process of fingerprint capturing. Furthermore, large variations in resolution and geometric distortion may exist among the fingerprint images acquired from different types of sensors. This distortion greatly challenges the traditional minutiae-based fingerprint matching algorithms. In this paper, we propose a novel ant colony optimization algorithm to establish minutiae correspondences in large-distorted fingerprints. First, minutiae similarity is measured by local features, and an assignment graph is constructed by local search. Then, the minutiae correspondences are established by a pseudo-greedy rule and local propagation, and the pheromone matrix is updated by the local and global update rules. Finally, the minutiae correspondences that maximize the matching score are selected as the matching result. To compensate resolution difference of fingerprint images captured from disparate sensors, a common resolution method is adopted. The proposed method is tested on FVC2004 DB1 and a FINGERPASS cross-matching database established by our lab. The experimental results demonstrate that the proposed algorithm can effectively improve the performance of large-distorted fingerprint matching, especially for those fingerprint images acquired from different modes of acquisition.     

Fingerprint image analysis for automatic identification

Most of the papers on fingerprints deal with classification of fingerprint images. Fingerprint databases being large (in the range of millions), the effort in matching of fingerprints within a class or when the class is unknown, is very significant. This requires fingerprint image analysis and extraction of the “minutiae” features, which are used for matching FPs. In this paper a scheme of preprocessing and feature extraction of fingerprint images for automatic identification is presented, which works even if the pattern class is unknown. The identification of fingerprints is based on matching the minutiae features of a given finger-print against those stored in the database. The core and delta information is used for classification and for registration while matching. These algorithms have been tested for more than 10,000 fingerprint images of different qualities. The results are manually verified and found to be very good for practical application. A few sample results are presented.     

A novel hierarchical fingerprint matching approach

Fingerprint matching is an important and essential step in automated fingerprint recognition systems (AFRSs). The noise and distortion of captured fingerprints and the inaccurate of extracted features make fingerprint matching a very difficult problem. With the advent of high-resolution fingerprint imaging techniques and the increasing demand for high security, sweat pores have been recently attracting increasing attention in automatic fingerprint recognition. Therefore, this paper takes fingerprint pore matching as an example to show the robustness of our proposed matching method to the errors caused by the fingerprint representation. This method directly matches pores in fingerprints by adopting a coarse-to-fine strategy. In the coarse matching step, a tangent distance and sparse representation-based matching method (denoted as TD-Sparse) is proposed to compare pores in the template and test fingerprint images and establish one-to-many pore correspondences between them. The proposed TD-Sparse method is robust to noise and distortions in fingerprint images. In the fine matching step, false pore correspondences are further excluded by a weighted RANdom SAmple Consensus (WRANSAC) algorithm in which the weights of pore correspondences are determined based on the dis-similarity between the pores in the correspondences. The experimental results on two databases of high-resolution fingerprints demonstrate that the proposed method can achieve much higher recognition accuracy compared with other state-of-the-art pore matching methods.     

基于奇异点邻近结构的快速指纹识别

将指纹识别中分类和匹配过程相结合,提出了一种包含奇异点周边的方向场和细节点等特征的奇异点邻近结构.该结构利用奇异点周边识别信息集中的特点,大大减少了匹配的计算量,并能够同时作为指纹分类和比对的特征,直接应用于指纹的连续分类和快速匹配过程,实现对大容量指纹数据库的快速识别.在NIST和FVC2004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(automatic fingerprint identification system,简称AFIS)的识别准确性的同时,还使得指纹在线识别系统的1:N辨识速度有显著的提高.     

Global ridge orientation modeling for partial fingerprint identification

Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.     

基于隐马尔可夫模型的指纹分类研究

指纹分类是针对大型指纹库的一个重要的索引方式，可以有效地提高指纹匹配的效率．指纹类型的不同表现为指纹纹理结构的差异，而指纹的方向场则可以有效地描述纹理结构的差异．同一类型指纹不同区域上方向角结构的差异以及相邻区域间方向角结构的联系可以视作一个马尔可夫随机场．本文利用嵌入式隐马尔可夫模型对指纹方向场进行建模分析，通过合理地抽取指纹的类型特征，构造观察向量、进行建模训练，然后利用训练好的马尔可夫模型进行匹配，最终提出并实现了一种新的鲁棒性强且精度较高的指纹分类方法．