Template Fingerprint Research Articles

Cryptographic Security Approach for Biometric Verification System

This paper presents cryptography which is the science of encryption and decryption to protect fingerprint that can be transmitted from sender to receiver. This security approach (cryptography) can also be applicable to other biometric traits like face, iris, retina and palm print. The significant of this protective medium is to prevent intruders or attacker to easily perceive the presence of fingerprint images. The method that was utilized for implementation of this cryptographic security approach for fingerprint verification System was achieved through Elliptic Curve Cryptography and Hill Cipher Algorithm. An elliptic Curve Function was defined and its domain parameters were used to generate self invertible key matrix that was used for the encryption and decryption process of the biometric images (Fingerprint, Face and Ear ) The security approach was also improved by ensuring that the decryption can only be done through secret key. After decryption, fingerprint verification was done by extracting and matching distinct feature (ridges) from template fingerprint and distinct feature from input fingerprint. The result obtained from demonstration of the cryptographic approach allows end users to load fingerprint image, encrypt it at sending end. At receiver end, end user can decrypt the fingerprint image. Image enhancement was later done before authentication through extraction and matching of distinct features. This model will prevent destruction and manipulation of stored fingerprint image. Authentication can also be accomplished through biological traits instead of use of password that can be stolen or transferred to relative.

Fingerprint pore matching using deep features

As a popular living fingerprint feature, sweat pore has been adopted to build robust high resolution automated fingerprint recognition systems (AFRSs). Pore matching is an important step in high resolution fingerprint recognition. This paper proposes a novel pore matching method with high recognition accuracy. The method mainly solves the pore representation problem in the state-of-the-art direct pore matching method. By making full use of the diversity and large quantities of sweat pores on fingerprints, deep convolutional networks are carefully designed to learn a deep feature (denoted as DeepPoreID) for each pore. The inter-class difference and intra-class similarity of pore patch pairs can be well solved using deep learning. The DeepPoreID is then used to describe the local feature for each pore and finally integrated into the classical direct pore matching method. More specifically, pore patches, which are cropped from both Query and Template fingerprint images, are imported into the well-trained networks to generate DeepPoreID for pore representation. The similarity between those DeepPoreIDs are then obtained by calculating the Euclidian Distance between them. Subsequently, one-to-many coarse pore correspondences are established via comparing their similarity. Finally, classical Weighted RANdom SAmple Consensus (WRANSAC) is employed to pick true pore correspondences from coarse ones. The experiments carried on the two public high resolution fingerprint database have shown the effectiveness of the proposed DeepPoreID, especially for fingerprint matching with small image size. Meanwhile, better recognition accuracy is achieved by the proposed method when compared with the existing state-of-the-art methods. About 35% rise in equal error rate (EER) and about 30% rise in FMR1000 when compared with the best result evaluated on the database with image size of 320 × 240 pixels.

Fingerprint Liveness Detection by a Template-Probe Convolutional Neural Network

Fingerprints are known to be easily synthesized to trick identification systems. In this paper, we propose a new method that incorporates template fingerprints stored for identification in the liveness detection system. The fingerprint identification platform must have a list of template fingerprints stored for matching with new probe fingerprints trying to access the system. Thus, instead of simply detecting the liveness of the probe fingerprints, the proposed approach uses the matching template fingerprints along with probe fingerprints through convolutional neural networks to make the liveness decision, which comprises two sequential convolutional neural networks for classification. The proposed method can be built on the top of existing liveness detection methods to increase accuracy without a significant increase in computation time. The evaluation over the LivDet dataset shows that the proposed fingerprint liveness detection method is able to obtain state-of-the-art accuracy.

Fingerprint verification using computational geometry

This paper presents a robust minutiae based method for fingerprint verification. The proposed method uses Delaunay Triangulation to represent minutiae as nodes of a connected graph composed of triangles. The minimum angle over all triangulations is maximized, which gives local stability to the constructed structures against rotation and translation variations. Geometric thresholds and minutiae data were used to characterize the triangulations created from input and template fingerprint images. The effectiveness of the proposed method is confirmed through calculations of false acceptance rate (FAR), false rejected rate (FRR) and equal error rate (EER) over FVC2002 databases compared to the results of other approaches.

A Contactless Fingerprint Verification Method using a Minutiae Matching Technique

Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.

Robust and Efficient Algorithms for Separating Latent Overlapped Fingerprints

Overlapped fingerprints are frequently encountered in latent fingerprints lifted from crime scenes. It is necessary to separate such overlapped fingerprints into component fingerprints so that existing fingerprint matchers can recognize them. The most crucial step in separating overlapped fingerprints is estimation of component orientation fields, which is a challenging problem for existing orientation field estimation algorithms. We propose a robust orientation field estimation algorithm (called the basic algorithm) for latent overlapped fingerprints whose core is the constrained relaxation labeling algorithm. We also propose improved versions of the basic algorithm for two special but frequent cases: 1) the mated template fingerprint of one component fingerprint is known and 2) the two component fingerprints are from the same finger. In both cases, further constraints are used to reduce ambiguity in relaxation labeling. Experimental results on both real and simulated overlapped fingerprints show that the proposed algorithm outperforms the state-of-the-art algorithm in both accuracy and efficiency. The two improved versions also perform better than the basic algorithm in respective cases. The latent overlapped fingerprint database collected for this study is made publicly available for performance evaluation.

A framework of multitemplate ensemble for fingerprint verification

How to improve performance of an automatic fingerprint verification system (AFVS) is always a big challenge in biometric verification field. Recently, it becomes popular to improve the performance of AFVS using ensemble learning approach to fuse related information of fingerprints. In this article, we propose a novel framework of fingerprint verification which is based on the multitemplate ensemble method. This framework is consisted of three stages. In the first stage, enrollment stage, we adopt an effective template selection method to select those fingerprints which best represent a finger, and then, a polyhedron is created by the matching results of multiple template fingerprints and a virtual centroid of the polyhedron is given. In the second stage, verification stage, we measure the distance between the centroid of the polyhedron and a query image. In the final stage, a fusion rule is used to choose a proper distance from a distance set. The experimental results on the FVC2004 database prove the improvement on the effectiveness of the new framework in fingerprint verification. With a minutiae-based matching method, the average EER of four databases in FVC2004 drops from 10.85 to 0.88, and with a ridge-based matching method, the average EER of these four databases also decreases from 14.58 to 2.51.

Unstructured Fingerprint Database Matching Method Based on Grover’s Quantum Algorithm

Traditional fingerprint matching algorithm has slow speed and low probability in search rate under large-scale and unstructured database background. In order to solve this problem, an advanced searching method based on Grover’s search algorithm is proposed. It improves the Oracle operator in Grover’s algorithm and transforms the database one into four registers: index register, quantum bit register which includes target fingerprint matching value, data register, 1 qubit register. Database two is transformed into including all the records of the matched value between input fingerprint and template fingerprints. Compared with traditional algorithm, this improved method can decrease the number of repetitions and raise the probability of success, thus it can effectively improve the search speed and accuracy.

Assessing Fingerprint Individuality in Presence of Noisy Minutiae

Fingerprint image quality is an important source of intraclass variability. When the underlying image quality is poor, human experts as well as automatic systems are more likely to make errors in minutiae detection and matching by either missing true features or detecting spurious ones. As a consequence, fingerprint individuality estimates change depending on the quality of the underlying images. The goal of this paper is to quantitatively study the effect of noise in minutiae detection and localization, resulting from varying image quality, on fingerprint individuality. The measure of fingerprint individuality is modeled as a function of image quality via a random effects model and methodology for the estimation of unknown parameters is developed in a Bayesian framework. Empirical results on two databases, one in-house and another publicly available, demonstrate how the measure of fingerprint individuality increases as image quality becomes poor. The measure corresponding to the ?12-point match? with 26 observed minutiae in the query and template fingerprints increases by several orders of magnitude when the fingerprint quality degrades from ?best? to ?poor?.

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.

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.

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.

A randomized approach with geometric constraints to fingerprint verification

In this paper, a fuzzy bipartite weighted graph model is proposed to solve fingerprint verification problem. A fingerprint image is preprocessed first to form clusters of feature points, which are called feature point clusters. Twenty-four attributes are extracted for each feature point cluster. The attributes are characterized by fuzzy values. Attributes of an input image to be verified are considered as the set of left nodes in a fuzzy bipartite weighted graph, and the attributes of claimed template fingerprint image are considered as the set of right nodes in the graph. The fingerprint verification problem is thus converted into a fuzzy bipartite weighted graph matching problem. A matching algorithm is proposed for the fuzzy bipartite weighted graph model to find an optimal matching with a goodness score. Experimental results reveal the feasibility of the proposed approach in fingerprint verification.