Fingerprint verification based on minutiae features: a review

Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint-based identification in the early 1960s. More recently, fingerprints have found an application in biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioural characteristics. Due to its security-related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. The main objective of this paper is to review the extensive research that has been done on automated fingerprint matching over the last four decades. In particular, the focus is on minutiae-based algorithms. Minutiae features contain most of a fingerprints individuality, and are consequently the most important fingerprint feature for verification systems. Minutiae extraction, matching algorithms, and verification performance are discussed in detail, with open problems and future directions identified.

Fingerprint minutiae extraction based on principal curves

It is important to extract minutiae of a fingerprint for the implementation of an auto fingerprint identification system. In this paper, the principal graph algorithm proposed by Kegl is used to obtain principal curves, which can be served as the skeletons of a fingerprint. Based on the obtained principal curves, a minutiae extraction algorithm is proposed to extract minutiae of the fingerprint. The experimental results indicate that principal curves obtained from the principal graph algorithm are smoother than the ones obtained from thinning algorithm, and the minutiae extracted by the proposed algorithm are more efficient.     

基于线模式的指纹细节点匹配算法

提出了一种新的基于线模式的指纹图像细节点匹配算法,这种算法先分别找出最佳匹配点对,确定相对旋转角度,较快地确定两个指纹的相关性,之后再搜索漏的匹配点对,去掉错误的匹配点对。算法受指纹增强、后处理的影响较小,细节点相似程度受其邻域细节点影响较小。介绍了算法的具体实现方法,并用实际指纹数据进行了测试。初步测试的结果表明,此算法是指纹图像细节点匹配的一种有效算法。     

Fingerprint indexing based on Minutia Cylinder-Code

This paper proposes a new hash-based indexing method to speed up fingerprint identification in large databases. A Locality-Sensitive Hashing (LSH) scheme has been designed relying on Minutiae Cylinder-Code (MCC), which proved to be very effective in mapping a minutiae-based representation (position/ angle only) into a set of fixed-length transformation-invariant binary vectors. A novel search algorithm has been designed thanks to the derivation of a numerical approximation for the similarity between MCC vectors. Extensive experimentations have been carried out to compare the proposed approach against 15 existing methods over all the benchmarks typically used for fingerprint indexing. In spite of the smaller set of features used (top performing methods usually combine more features), the new approach outperforms existing ones in almost all of the cases.     

基于Fuzzy Vault方案的指纹细节特征加密

指出了现有指纹Fuzzy Vault方法用于加密长度较短的密钥时安全性不足的问题,并基于Fuzzy Vault方案,提出了一种指纹细节特征加密方法.首先指定Fuzzy Vault编码多项式的幂次,并根据指定幂次对加密密钥进行处理和隐藏.通过这种处理,避开了现有方法中密钥长度决定Fuzzy Vault编码多项式幂次的弊端,解除了现有方法加密长度较短的密钥时所存在的安全隐患.     

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 minutiae filtering using ARTMAP

Fingerprints are widely used for unique personal identification based on minutiae matching. Minutiae are the terminations and bifurcations of ridges in a fingerprint image. Generally fingerprint images are of low quality due to the presence of noise and contrast deficiency resulting in discontinuity in ridges producing false minutiae points. It is worth noting that there is a fundamental difference between a neural network (NN) approach for minutiae location and minutiae filtering. In this paper, the spurious minutiae points and the bug pixels introduced during the thinning process are eliminated based on the neighborhood pixel information. A new minutiae filtering algorithm using a NN is introduced to improve the accuracy of the extraction algorithm proposed in the literature. Each minutia, as detected by the algorithm, is classified through ARTMAP NN whose output indicates whether it is a termination, a bifurcation or a false minutia. Experimental results show that the efficiency of minutiae classification has significantly improved using the proposed filtering algorithm.     

Fingerprint matching from minutiae texture maps

The fingerprint matching using the original FingerCode generation has proved its effectiveness but it suffers from some limitations such as the reference point localization and the recourse to the relative fingerprint pre-alignment stage. In this paper, we propose a new hybrid fingerprint matching technique based on minutiae texture maps according to their orientations. Therefore, rather than exploiting the eight fixed directions of Gabor filters for all original fingerprint images filtering process, we construct absolute images starting from the minutiae localizations and orientations to generate our weighting oriented Minutiae Codes. The extracted features are invariant to translation and rotation, which allows us avoiding the fingerprint pair relative alignment stage. Results are presented demonstrating significant improvements in fingerprint matching accuracy through public fingerprint databases.     

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.     

基于B样条曲线的指纹Fuzzy Vault对齐方法

指纹设备采集到的指纹图像由于不能用统一的参照表示指纹细节点的坐标,因此,同一枚指纹分别在录入和注册时得到的指纹细节点需要对齐。本文采用指纹纹线作为指纹Fuzzy vault对齐的方法,在目标纹线上提取B样条控制点,用这些控制点数据与纹线的距离作为辅助数据来对齐指纹模板。研究分析表明这种方法不会泄露指纹细节点的任何信息。实验结果证明,这种方法具有很好的实时性并能提高指纹Fuzzy vault的配准率。     

结合方向图的指纹特征点提取

报告了指纹识别研究的现状,分析了指纹识别过程中伪特征点对识别结果的影响,以及指纹识别过程中不同伪特征产生的原因.对指纹图像进行二值化,细化等预处理,并初步提取特征点.结合指纹方向图,对从细化图像中提取出的大量特征点进行处理,利用不同伪特征的不同方向和距离特点设计了伪特征点判断和删除的判据,在去除伪特征点的同时又保证真正的特征点不被误删.实验结果表明,该方法取得了较好的效果,能满足实际使用的需要.     

Fingerprint minutiae matching algorithm for real time system

Many fingerprint matching algorithms have been reported in articles in recent years. And people did fingerprint images matching through minutiae matching in most of the algorithms. In this paper, we proposed a new fingerprint minutiae matching algorithm, which is fast, accurate and suitable for the real time fingerprint identification system. In this algorithm we used the core point to determine the reference point and used a round bounding box for matching. Experiments done on a set of fingerprint images captured with a scanner showed that our algorithm is faster and more accurate than Xiping Luo's algorithm.     

Fingerprint indexing with bad quality areas

Fingerprint indexing is a key technique in fingerprint identification systems. This strategy allows us to reduce the search space and the occurrences of false acceptance in databases with great size. This paper presents a new triplet based indexing algorithm which uses a new fingerprint representation, based on minutia triplets. This representation is an extension of the triangle set obtained from Delaunay triangulation. Also, a strategy is proposed in order to dismiss bad quality triplets that could affect the accuracy of the indexing process. This proposal shows a good accuracy, even when the fingerprints have bad quality areas.     

Fingerprint classification based on Adaboost learning from singularity features

Fingerprint classification is an important indexing scheme to narrow down the search of fingerprint database for efficient large-scale identification. It is still a challenging problem due to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. In this paper, we presents a fingerprint classification algorithm that uses Adaboost learning method to model multiple types of singularity features. Firstly, complex filters are used to detect the singularities. For powerful representation, we compute the complex filter responses of the detected singularities at multiple scales and a feature vector is constructed for each scale that consists of the relative position and direction and the certainties of the singularities. Adaboost learning method is then applied on decision trees to design a classifier for fingerprint classification. Finally, fingerprint class is determined by the ensemble of the classification results at multiple scales. The experimental results and comparisons on NIST-4 database have shown the effectiveness and superiority of the fingerprint classification algorithm.     

Method for fingerprint minutiae matching based on their alignment

In the paper a method for fingerprint matching is presented, which is based on the use of additional information about the representation of fingerprint minutiae. The method proposed is a modification of the method by M. Tico et al [1]. However, in contrast with the prototype, this method uses both the characteristic of local regions of the directional field and the local configurations of minutiae present in the fingerprint. Vladimir Nikolaevich Perminov. Graduated from the Moscow State Institute of Electronic Technology. Candidate of Engineering. General Director of UniqueICs Company. Scientific interests: systems of computer-aided design of integrated schemes and simulation methods; image processing and compression, pattern recognition. Aleksei Mikhailovich Fartukov. Born 1979. Graduated from the Moscow State Institute of Electronic Technology in 2001. Works as engineer-programmer inUniqueICs Company. Scientific interests: image processing and compression, pattern recognition.     

Alphabet indexing for approximating features of symbols

We consider two maximization problems to find a mapping from a large alphabet forming given two sets of strings to a set of a very few symbols specifying a symbol wise transformation of strings. First we show that the problem to find a mapping that transforms the most of the strings as to form disjoint sets cannot be approximated within a ratio n^1/16 in polynomial time, unless P = NP. Next we consider a mapping that retains the difference of the maximum number of pairs of strings over the given sets. We present a polynomial-time approximation algorithm that guarantees a ratio k/(k − 1) for mappings to k symbols, as well as proving that the problem is hard to approximate within an arbitrary small ratio in polynomial time. Furthermore, we extend this algorithm as to deal with not only pairs but also tuples of strings and show that it achieves a constant approximation ratio.     

Alignment-free cancelable fingerprint templates based on local minutiae information.

To replace compromised biometric templates, cancelable biometrics has recently been introduced. The concept is to transform a biometric signal or feature into a new one for enrollment and matching. For making cancelable fingerprint templates, previous approaches used either the relative position of a minutia to a core point or the absolute position of a minutia in a given fingerprint image. Thus, a query fingerprint is required to be accurately aligned to the enrolled fingerprint in order to obtain identically transformed minutiae. In this paper, we propose a new method for making cancelable fingerprint templates that do not require alignment. For each minutia, a rotation and translation invariant value is computed from the orientation information of neighboring local regions around the minutia. The invariant value is used as the input to two changing functions that output two values for the translational and rotational movements of the original minutia, respectively, in the cancelable template. When a template is compromised, it is replaced by a new one generated by different changing functions. Our approach preserves the original geometric relationships (translation and rotation) between the enrolled and query templates after they are transformed. Therefore, the transformed templates can be used to verify a person without requiring alignment of the input fingerprint images. In our experiments, we evaluated the proposed method in terms of two criteria: performance and changeability. When evaluating the performance, we examined how verification accuracy varied as the transformed templates were used for matching. When evaluating the changeability, we measured the dissimilarities between the original and transformed templates, and between two differently transformed templates, which were obtained from the same original fingerprint. The experimental results show that the two criteria mutually affect each other and can be controlled by varying the control parameters of the changing functions.     

Models from image triplets using epipolar gradient features

In an application where sparse matching of feature points is used towards fast scene reconstruction, the choice of the type of features to be matched has an important impact on the quality of the resulting model. In this work, a method is presented for quickly and reliably selecting and matching points from three views of a scene. The selected points are based on epipolar gradients, and consist of stable image features relevant to reconstruction. Then, the selected points are matched using edge transfer, a measure of geometric consistency for point triplets and the edges on which they lie. This matching scheme is tolerant to image deformations due to changes in viewpoint. Models drawn from matches obtained by the proposed technique are shown to demonstrate its usefulness.