Minutiae Matching Research Articles

Latent Palmprint Matching

The evidential value of palmprints in forensic applications is clear as about 30 percent of the latents recovered from crime scenes are from palms. While biometric systems for palmprint-based personal authentication in access control type of applications have been developed, they mostly deal with low-resolution (about 100 ppi) palmprints and only perform full-to-full palmprint matching. We propose a latent-to-full palmprint matching system that is needed in forensic applications. Our system deals with palmprints captured at 500 ppi (the current standard in forensic applications) or higher resolution and uses minutiae as features to be compatible with the methodology used by latent experts. Latent palmprint matching is a challenging problem because latent prints lifted at crime scenes are of poor image quality, cover only a small area of the palm, and have a complex background. Other difficulties include a large number of minutiae in full prints (about 10 times as many as fingerprints), and the presence of many creases in latents and full prints. A robust algorithm to reliably estimate the local ridge direction and frequency in palmprints is developed. This facilitates the extraction of ridge and minutiae features even in poor quality palmprints. A fixed-length minutia descriptor, MinutiaCode, is utilized to capture distinctive information around each minutia and an alignment-based minutiae matching algorithm is used to match two palmprints. Two sets of partial palmprints (150 live-scan partial palmprints and 100 latent palmprints) are matched to a background database of 10,200 full palmprints to test the proposed system. Despite the inherent difficulty of latent-to-full palmprint matching, rank-1 recognition rates of 78.7 and 69 percent, respectively, were achieved in searching live-scan partial palmprints and latent palmprints against the background database.

Upper core point detection using improved ant colony optimization algorithm

Minutiae detection is a crucial process in an automatic fingerprint identification system. Most fingerprint comparison algorithms are based on minutiae matching. However, the local orientation changes very rapidly in the singular point area. It is difficult to locate the singular point precisely. The Ant Colony Optimization Algorithm (ACOA) is extensively used in multi-objective and optimal problems. But the ACOA is still not used in fingerprint image processing. In this paper, we suggest an improved Ant Colony Optimization Algorithm to extract the upper core point of fingerprints. Finally, the proposed algorithms are tested with some fingerprint images and show significant improvement in the experiments.

A compensation scheme of fingerprint distortion using combined radial basis function model for ubiquitous services

Pervasive computing has explosive impact in the application, including security and accessibility of distributed systems. With the increasing demand of information security and accessibility, more and more distributed systems use fingerprint identification technique as the access control. Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. In this paper, according to the particularity of fingerprint distortion, we propose a combined radial basis function (RBF) model, which separately builds rigid and nonrigid transformations, for attacking the distortion problem. Combined RBF model provides more accurate mapping function between a possible matched-pair. Experiments on real data demonstrate the efficacy of the proposed method in improving the compensation of fingerprint distortion. Also, the security and accessibility of the system using our method are further enhanced.

Local binary patterns for a hybrid fingerprint matcher

In this work, we present a novel hybrid fingerprint matcher system based on local binary patterns. The two fingerprints to be matched are first aligned using their minutiae, then the images are decomposed in several overlapping sub-windows, each sub-window is convolved with a bank of Gabor filters and, finally, the invariant local binary patterns histograms are extracted from the convolved images. Extensive experiments conducted over the four FVC2002 fingerprint databases show the effectiveness of the proposed hybrid approach with respect to the well-known Tico's minutiae matcher and other image-based approaches. Moreover, a BioHashing approach have been designed using the proposed fixed-length feature vector and very interesting performance has been obtained by combining it with the Tico's minutiae matcher.

Fingerprint image enhancement and postprocessing based on the directional fields

Fingerprint recognition is based on minutiae matching. The matching correctness of the fingerprints is due to the effect of the accuracy of the minutiae. Fingerprint enhancement and postprocessing are used to reduce the false minutiae. In this paper, we propose methods on fingerprint enhancement and postprocessing, based on the directional fields of a fingerprint. We directly enhance the fingerprint on a gray-scale image and reduce most false minutiae in the postprocessing step. The achieved results are compared with other methods, and the reduction of false minutiae and the recovery of dropped minutiae are improved.

Fingerprint-Based Fuzzy Vault: Implementation and Performance

Reliable information security mechanisms are required to combat the rising magnitude of identity theft in our society. While cryptography is a powerful tool to achieve information security, one of the main challenges in cryptosystems is to maintain the secrecy of the cryptographic keys. Though biometric authentication can be used to ensure that only the legitimate user has access to the secret keys, a biometric system itself is vulnerable to a number of threats. A critical issue in biometric systems is to protect the template of a user which is typically stored in a database or a smart card. The fuzzy vault construct is a biometric cryptosystem that secures both the secret key and the biometric template by binding them within a cryptographic framework. We present a fully automatic implementation of the fuzzy vault scheme based on fingerprint minutiae. Since the fuzzy vault stores only a transformed version of the template, aligning the query fingerprint with the template is a challenging task. We extract high curvature points derived from the fingerprint orientation field and use them as helper data to align the template and query minutiae. The helper data itself do not leak any information about the minutiae template, yet contain sufficient information to align the template and query fingerprints accurately. Further, we apply a minutiae matcher during decoding to account for nonlinear distortion and this leads to significant improvement in the genuine accept rate. We demonstrate the performance of the vault implementation on two different fingerprint databases. We also show that performance improvement can be achieved by using multiple fingerprint impressions during enrollment and verification.

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.

Local relative location error descriptor-based fingerprint minutiae matching

Minutiae matching with non-linear distortion is a challenging task in an Automatic Fingerprint Identification System. In this paper, a feature called Local Relative Location Error Descriptor (LRLED) is proposed to overcome non-linear distortion. The LRLED-based algorithm consists of three procedures. Firstly, a pairwise alignment method is proposed to achieve fingerprint alignment. Secondly, a matched minutiae-pair set is obtained with a comparatively loose threshold to reduce false non-matches, which lead not only to most of the corresponding minutiae-pairs, but also to a few non-corresponding minutiae-pairs getting matched. Finally, the LRLED-based similarity measure, which outputs a very high score for a corresponding minutiae-pair but a very low score for a non-corresponding minutiae-pair to reduce false matches, is employed to compute the similarity level between template and test fingerprints. Evaluations on FVC2002 and FVC2004 databases reveal that LRLED is good at distinguishing between corresponding and non-corresponding minutiae-pairs and works well for fingerprint minutiae matching.

Advanced methods for two-class pattern recognition problem formulation for minutiae-based fingerprint verification

We present a new method for minutiae-based fingerprint verification that approaches the problem as a two-class pattern recognition problem. In our knowledge, this is one of the first works that uses as features for fingerprint verification the response of a minutiae matcher between two fingerprint. The feature vector obtained by the minutiae matching is classified into “genuine” or “impostor” by support vector machines. Results from FVC2002 are presented, yielding remarkable performance improvement with respect to other state-of-the-art approaches.

Rotationally invariant statistics for examining the evidence from the pores in fingerprints

Recent methodological advances in the processing of DNA evidence have begun to force a closer examination of assertions about the strength of other sorts of evidence. One traditional source of evidence is the fingerprint. Currently a print taken from a suspect is compared against a mark from a crime scene and a match declared using the judgement of an expert based on matching minutiae and the ridge patterns around these. However, such methods have proved difficult to quantify effectively. This has provoked the investigation of even finer features in the print and the mark. One set of such features are the many pores, located along the ridges of the fingerprint. Is it possible to supplement expert judgements associated with a match with a more automatic and quantitative measure of the strength of evidence, based on pore information? The results of this preliminary analysis suggest we can. Our methodology is relatively transparent, using common statistics for two sample comparisons of point patterns. The results discussed here concern the matching of inked prints using grey-level imaging and complement previous studies which tend to focus on the comparison of binarised images.

Combining minutiae descriptors for fingerprint matching

A novel minutiae-based fingerprint matching algorithm is proposed. A minutiae matching algorithm has to solve two problems: correspondence and similarity computation. For the correspondence problem, we assign each minutia two descriptors: texture-based and minutiae-based descriptors, and use an alignment-based greedy matching algorithm to establish the correspondences between minutiae. For the similarity computation, we extract a 17-D feature vector from the matching result, and convert the feature vector into a matching score using support vector classifier. The proposed algorithm is tested on FVC2002 databases and compared to all participators in FVC2002. According to equal error rate, the proposed algorithm ranks 1st on DB3, the most difficult database in FVC2002, and on the average ranks 2nd on all 4 databases.

Fingerprint verification using ridge direction distribution and minutiae correspondence

AbstractFingerprint verification technique is raising high expectations as a means of personal identification based on physical features. This paper proposes a fingerprint verification method which can achieve minutiae matching with highly accurate compensation for rotation and position deviation, as well as high speed and stability. The process flow after binarization of the gray‐scale fingerprint image is as follows. (1) Using the fingerprint ridge direction distribution in each local region, highly accurate compensation for the rotation and the position deviation between the enrolled fingerprint and the input fingerprint is performed. (2) The optimal correspondence between enrolled and input minutiae is established by fast combinatorial search. (3) By applying threshold processing to the distances of the fingerprint ridge direction distributions and the minutiae matching rate, the acceptance/rejection of the person is decided. Fingerprint image data for 80 persons×4 fingers×10 samples were collected using a commercial electrostatic capacitance semiconductor fingerprint sensor. The false reject rate and the false acceptance rate are investigated in fingerprint verification experiments, and the effectiveness of the proposed method is demonstrated. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(3): 72–82, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10522

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 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 MINUTIAE RELATIONSHIP REPRESENTATION AND MATCHING BASED ON CURVE COORDINATE SYSTEM

A minutiae relationship representation and matching method based on curve coordinate system is proposed. For each minutia, a curve coordinate system is established, and the coordinates of other minutiae in this coordinate system is computed. Thus, the coordinate relationship between each pair of minutiae can be evaluated. These relationships are used for pairing minutiae between the template fingerprint and the query fingerprint by means of transferring reference minutiae. The algorithm is tested on FVC2004DBs which include many highly distorted fingerprints. Results have shown that the proposed algorithm achieves improved matching accuracy and is able to cope with highly distorted fingerprints.

DETERMINATION OF MINUTIAE SCORES FOR FINGERPRINT IMAGE APPLICATIONS

Many Automatic Fingerprint Identification Systems (AFIS) are based on minutiae matching. Minutiae are the terminations and bifurcations of the ridge lines in a fingerprint image. A gray-scale fingerprint image that has undergone binarization, followed by thinning, in order to extract the minutiae, may contain hundreds of minutiae, all of which are not so vivid and obvious in the original image. Thus, the set of minutiae that are well-defined and more prominent than the rest should be given higher relevance and importance in the process of minutiae matching. In this work, a gray-scale fingerprint image is first preprocessed to produce a thinned binary image. Next, a method to assign a score value to each of the extracted minutiae is proposed, based on certain topographical properties of a minutia. The score associated to a minutia signifies its robustness and prominence. A minutia with a higher score value should be considered with higher priority in the matching scheme to yield better results. Experimental results on several standard databases have been reported.

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 matching using the adjacent feature vector

Minutiae matching is the most popular approach to fingerprint verification. In this paper, we propose a novel fingerprint feature named the adjacent feature vector (AFV) for fingerprint matching. An AFV consists of four adjacent relative orientations and six ridge counts of a minutia. Given a fingerprint image, the optimal matching score is computed in three stages: (1) minutiae candidate pairs searching based on AFVs; (2) coordinate transform for image rotation and translation; and (3) transformed minutiae matching to get matching score. The experimental results show that the proposed method provides a good trade-off between speed and accuracy.

Fingerprint matching based on local relative orientation field

A fingerprint matching method based on local relative orientation field is proposed. It extracts local relative orientation field around each minutia for minutiae matching. Local orientation features are also used to sorting minutiae in order to speed up searching a minutia when pairing minutiae. The experimental result reveals that this method achieves improved recognition accuracy.

특징점의 연결정보를 이용한 지문인식

지문영상의 품질 향상과 특징점 정합은 자동 지문인식 시스템의 중요한 두 단계이다. 본 논문에서는 특징점의 연결정보를 사용한 지문인식 기법을 제안한다. 인식 과정은 전처리와 특징점 추출, 그리고 특징점 pairing을 기반으로 한 정합의 세 단계로 이루어져 있다. 정확성을 위해 세선화된 이미지로부터 지문의 특징점을 추출한 후에, 특징점의 연결정보를 사용한 정합과정을 소개한다. 특징점 정합과정에서 연결정보를 사용하는 것은 간단하지만 정확한 방법이며, 두 지문의 비교단계에서 낮은 비용으로 기준 특징점 쌍을 선택하는 문제를 해결해 준다. 알고리즘은 지문의 회전과 이동에 무관하다. 정합 알고리즘은 반도체 칩방식 지문 입력장치로부터 획득한 500개의 지문영상으로 실험하였으며, 실험 결과는 기존 방법보다 오인식율은 줄어들고 정확도는 증가하였음을 보여준다. Fingerprint image enhancement and minutiae matching are two key steps in an automatic fingerprint identification system. In this paper, we propose a fingerprint recognition technique by using minutiae linking information. Recognition process have three steps ; preprocessing, minutiae extraction, matching step based on minutiae pairing. After extracting minutiae of a fingerprint from its thinned image for accuracy, we introduce matching process using minutiae linking information. Introduction of linking information into the minutiae matching process is a simple but accurate way, which solves the problem of reference minutiae pair selection with low cost in comparison stage of two fingerprints. This algorithm is invariable to translation and rotation of fingerprint. The matching algorithm was tested on 500 images from the semiconductor chip style scanner, experimental result revealed the false acceptance rate is decreased and genuine acceptance rate is increased than existing method.