Fingerprint Verification Algorithm Research Articles

Biometric technology for personal identification and authentication: fingerprint

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

極座標サンプリングを用いた指紋照合アルゴリズム

極座標サンプリングを用いた指紋照合アルゴリズム

A hybrid biometric identification framework for high security applications

Research on biometrics for high security applications has not attracted as much attention as civilian or forensic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analysis and identification of the problems to be solved in order to meet the performance requirements for high security applications, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false acceptance rate (FAR) and then use the hybrid ensemble framework makes the false reject rate (FRR) as low as possible. Three experiments are performed to verify the effectiveness and generalization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is significantly lower than other state of the art methods. Second, in face verification, the framework also results in a large reduction in incorrect classification. Finally, assessing the performance of the framework on a combination of face and gait verification using a heterogeneous database show this framework can achieve both 0% false rejection and 0% false acceptance simultaneously.

A Joint FED Watermarking System Using Spatial Fusion for Verifying the Security Issues of Teleradiology

Teleradiology allows transmission of medical images for clinical data interpretation to provide improved e-health care access, delivery, and standards. The remote transmission raises various ethical and legal issues like image retention, fraud, privacy, malpractice liability, etc. A joint FED watermarking system means a joint fingerprint/encryption/dual watermarking system is proposed for addressing these issues. The system combines a region based substitution dual watermarking algorithm using spatial fusion, stream cipher algorithm using symmetric key, and fingerprint verification algorithm using invariants. This paper aims to give access to the outcomes of medical images with confidentiality, availability, integrity, and its origin. The watermarking, encryption, and fingerprint enrollment are conducted jointly in protection stage such that the extraction, decryption, and verification can be applied independently. The dual watermarking system, introducing two different embedding schemes, one used for patient data and other for fingerprint features, reduces the difficulty in maintenance of multiple documents like authentication data, personnel and diagnosis data, and medical images. The spatial fusion algorithm, which determines the region of embedding using threshold from the image to embed the encrypted patient data, follows the exact rules of fusion resulting in better quality than other fusion techniques. The four step stream cipher algorithm using symmetric key for encrypting the patient data with fingerprint verification system using algebraic invariants improves the robustness of the medical information. The experiment result of proposed scheme is evaluated for security and quality analysis in DICOM medical images resulted well in terms of attacks, quality index, and imperceptibility.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements.

Automatic Fingerprint Verification Algorithm Optimized Implemented on Embedded System

This paper introduces a solution that implement automatic fingerprint verification algorithm on embedded system. It described all parts of the system in details, including the hardware designing and software designing. In order to resolve the difficulty of applying image processing to embedded system, this paper presents an effective resolution and it will bring instruction to embedded system designer.

Unconditionally provably secure cancellable biometrics based on a quotient polynomial ring

Biometric authentication has attracted attention because of its high security and usability. However, biometric features such as fingerprints are unchangeable throughout the life of individuals. Thus, once biometric data have been compromised, they cannot be used for authentication securely ever again. To address this issue, an authentication scheme called cancellable biometrics has been studied. However, there remains a major challenge to achieve both strong security and practical accuracy. The correlation invariant random filtering (CIRF) is an algorithm for cancellable biometrics with provable security and practical accuracy. However, the security proof requires an unrealistically strong assumption with regard to biometric features. The authors examine the security of the CIRF when the assumption is not satisfied, and show that there are vulnerabilities. To address the problems, the authors interpret the CIRF from an algebraic point of view and generalise it based on a quotient polynomial ring. Then several theorems are proved, which derive a new transformation algorithm. The security of the algorithm without any condition on the biometric features is proved. The authors also evaluate the accuracy of the algorithm by applying it to the chip matching algorithm for fingerprint verification and show that it does not degrade the matching accuracy.

Fingerprint Matching Based on Orientation Feature

This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

Minutiae Extraction Based on Propriety of Curvature

Fingerprint verification algorithms need two majors steps; enhancement of image and minutiae extraction. Our approach uses Short Time Fourier Transform (STFT) Analysis to enhance fingerprint. The foreground region mask, local ridge orientation and local frequency are all estimated by STFT algorithm. The novel approach of minutiae extraction is based on the extraction of the map contour of fingerprint and then finding minutiae using the global and local proprieties of contour. We compare our proposed approach with the alternative approach of minutiae extraction and the results show the performance of our technique.

Online Fingerprint Verification Algorithm and Distributed System

In this paper, a novel online fingerprint verification algorithm and distribution system is proposed. In the beginning, fingerprint acquisition, image preprocessing, and feature extraction are conducted on workstations. Then, the extracted feature is transmitted over the internet. Finally, fingerprint verification is processed on a server through web-based database query. For the fingerprint feature extraction, a template is imposed on the fingerprint image to calculate the type and direction of minutiae. A data structure of the feature set is designed in order to accurately match minutiae features between the testing fingerprint and the references in the database. An elastic structural feature matching algorithm is employed for feature verification. The proposed fingerprint matching algorithm is insensitive to fingerprint image distortion, scale, and rotation. Experimental results demonstrated that the matching algorithm is robust even on poor quality fingerprint images. Clients can remotely use ADO.NET on their workstations to verify the testing fingerprint and manipulate fingerprint feature database on the server through the internet. The proposed system performed well on benchmark fingerprint dataset.

Enhancement of feature extraction for low-quality fingerprint images using stochastic resonance

This paper presents a new approach to enhancing feature extraction for low-quality fingerprint images by adding noise to the original signal. Feature extraction often fails for low-quality fingerprint images obtained from excessively dry or wet fingers. In nonlinear signal processing systems, a moderate amount of noise can help amplify a faint signal while excessive amounts of noise can degrade the signal. Stochastic resonance (SR) refers to a phenomenon where an appropriate amount of noise added to the original signal can increase the signal-to-noise ratio. Experimental results show that Gaussian noise added to low-quality fingerprint images enables the extraction of useful features for biometric identification. SR was applied to 20 fingerprint images in the FVC2004 DB2 database that were rejected by a state-of-the-art fingerprint verification algorithm due to failures in feature extraction. SR enabled feature extraction from 10 out of 11 low-quality images with poor contrast. The remaining nine images were damaged fingerprints from which no meaningful features can be obtained. Improved feature extraction using SR decreases an equal error rate of fingerprint verification from 6.55% to 5.03%. The receiver operating characteristic curve shows that the genuine acceptance rates are improved for all false acceptance rates.

GUC100 Multisensor Fingerprint Database for In-House (Semipublic) Performance Test

For evaluation of biometric performance of biometric components and system, the availability of independent databases and desirably independent evaluators is important. Both databases of significant size and independent testing institutions provide the precondition for fair and unbiased benchmarking. In order to show generalization capabilities of the system under test, it is essential that algorithm developers do not have access to the testing database, and thus the risk of tuned algorithms is minimized. In this paper, we describe the GUC100 multiscanner fingerprint database that has been created for independent and in-house (semipublic) performance and interoperability testing of third party algorithms. The GUC100 was collected by using six different fingerprint scanners (TST, L-1, Cross Match, Precise Biometrics, Lumidigm, and Sagem). Over several months, fingerprint images of all 10 fingers from 100 subjects on all 6 scanners were acquired. In total, GUC100 contains almost 72.000 fingerprint images. The GUC100 database enables us to evaluate various performances and interoperability settings by taking into account different influencing factors such as fingerprint scanner and image quality. The GUC100 data set is freely available to other researchers and practitioners provided that they conduct their testing in the premises of the Gjovik University College in Norway, or alternatively submit their algorithms (in compiled form) to run on GUC100 by researchers in Gjovik. We applied one public and one commercial fingerprint verification algorithm on GUC100, and the reported results indicate that GUC100 is a challenging database.

Fingerprint verification using statistical descriptors

The importance of high precision matching in fingerprint cannot be over-emphasized. This paper presents a novel fingerprint verification algorithm which improves matching accuracy by overcoming the shortcomings of poor image quality. The proposed method involves determination of a singular point using orientation field reliability, extraction of a square-sub-image (SSI); 129x129 pixels, statistical analysis of the co-occurrence matrices as well as application of dual analyses on experimental results; Pattern Recognition and Image Processing Laboratory (FVC2002) testing protocol and Program for Rate Estimation and Statistical Summaries (PRESS). The efficiency of the proposed method has been demonstrated by the experimental results which show equal error rate (EER) of 28% and a comparatively more accurate and robust means for reliable fingerprint verification.

Integrating Fingerprint Verification into the Smart Card-Based Healthcare Information System

As VLSI technology has been improved, a smart card employing 32-bit processors has been released, and more personal information such as medical, financial data can be stored in the card. Thus, it becomes important to protect personal information stored in the card. Verification of the card holder's identity using a fingerprint has advantages over the present practices of Personal Identification Numbers (PINs) and passwords. However, the computational workload of fingerprint verification is much heavier than that of the typical PIN-based solution. In this paper, we consider three strategies to implement fingerprint verification in a smart card environment and how to distribute the modules of fingerprint verification between the smart card and the card reader. We first evaluate the number of instructions of each step of a typical fingerprint verification algorithm, and estimate the execution time of several cryptographic algorithms to guarantee the security/privacy of the fingerprint data transmitted in the smart card with the client-server environment. Based on the evaluation results, we analyze each scenario with respect to the security level and the real-time execution requirements in order to implement fingerprint verification in the smart card with the client-server environment.

Combining pores and ridges with minutiae for improved fingerprint verification

This paper presents a fast fingerprint verification algorithm using level-2 minutiae and level-3 pore and ridge features. The proposed algorithm uses a two-stage process to register fingerprint images. In the first stage, Taylor series based image transformation is used to perform coarse registration, while in the second stage, thin plate spline transformation is used for fine registration. A fast feature extraction algorithm is proposed using the Mumford–Shah functional curve evolution to efficiently segment contours and extract the intricate level-3 pore and ridge features. Further, Delaunay triangulation based fusion algorithm is proposed to combine level-2 and level-3 information that provides structural stability and robustness to small changes caused due to extraneous noise or non-linear deformation during image capture. We define eight quantitative measures using level-2 and level-3 topological characteristics to form a feature supervector. A 2 ν -support vector machine performs the final classification of genuine or impostor cases using the feature supervectors. Experimental results and statistical evaluation show that the feature supervector yields discriminatory information and higher accuracy compared to existing recognition and fusion algorithms.

Paradigm Shift in Fingerprint Recognition on Pressure Variation and Impact of Information System in Crime Reduction

Fingerprint identification from a random offline fingerprint image has become a very actively studied field in biometric systems. This paper investigates the possibility to prepare a framework of applying the latent fingerprint obtained from a crime scene to be shared in a central data base. These fingerprints may be extremely useful to reduce the crimes from a country. In this approach each citizen is supposed to put his/her fingerprint in an organization during entry. This fingerprint will be matched to the central database for checking whether the fingerprint of that citizen matched with any finger print taken from a crime scene. The information system of an organization thus will be able to reveal the suspicious citizens causing to be investigated for further action. The quality of the fingerprint images greatly affects the performance of the minutiae extraction. The photo graph taken from different distances, uneven surface, different finger pressure, dust particles pose problems during recognition process. Improper finger pressure and uneven surface are the major cause to produce breaks in curves in a fingerprint. In order to improve the performance of the system, many researchers have been made efforts on the image enhancement algorithms. Most of the fingerprint recognition algorithms are based on minutia matching features. Therefore, minutiae extraction is one of the important steps in fingerprint verification algorithms. In this paper we present an algorithm to fill the broken curves on a fingerprint due to low finger pressure and uneven surface. Our proposed approach eliminates false minutiae that connect broken curves in fingerprint.

A fingerprint verification algorithm using tessellated invariant moment features

In this paper, an enhanced image-based fingerprint verification algorithm is proposed to improve matching accuracy and processing speed by overcoming the demerits of previous methods over poor-quality images. It reduces multi-spectral noise by enhancing a fingerprint image to accurately and reliably determine a reference point, and then aligns the image according to the position and orientation of reference point to avoid time-consuming alignment. A set of fixed-length moment features, invariant to the affine transform, is extracted from tessellated cells on a region of interest (ROI) centered at the reference point. The similarity between an input and a template in a database is evaluated by eigenvalue-weighted cosine (EWC) distance. Experimental results show that the proposed method has better performance in accuracy and speed comparing with other renowned methods.

A Memetic Fingerprint Matching Algorithm

Minutiae point pattern matching is the most common approach for fingerprint verification. Although many minutiae point pattern matching algorithms have been proposed, reliable automatic fingerprint verification remains as a challenging problem, both with respect to recovering the optimal alignment and the construction of an adequate matching function. In this paper, we develop a memetic fingerprint matching algorithm (MFMA) which aims to identify the optimal or near optimal global matching between two minutiae sets. Within the MFMA, we first introduce an efficient matching operation to produce an initial population of local alignment configurations by examining local features of minutiae. Then, we devise a hybrid evolutionary procedure by combining the use of the global search functionality of a genetic algorithm with a local improvement operator to search for the optimal or near optimal global alignment. Finally, we define a reliable matching function for fitness computation. The proposed algorithm was evaluated by means of a series of experiments conducted on the FVC2002 database and compared with previous work. Experimental results confirm that the MFMA is an effective and practical matching algorithm for fingerprint verification. The algorithm is faster and more accurate than a traditional genetic-algorithm-based method. It is also more accurate than a number of other methods implemented for comparison, though our method generally requires more computational time in performing fingerprint matching.

Orientation feature for fingerprint matching

This paper describes a fingerprint verification algorithm based on the orientation field. The orientation field of a fingerprint image has also been used for image alignment. Area around the core point has been employed as an area of interest for determining the orientation feature map. The algorithm has been tested on two databases (database available from University of Bologna, Biometrics Laboratory and FVC2002). The performance of the algorithm is measured in terms of receiver operating characteristics (ROC). For the University of Bologna database, at ∼ 0 % false acceptance rate (FAR) the genuine acceptance rate (GAR) observed is ∼ 78 % and at ∼ 11 % FAR, GAR is ∼ 97 % . For the FVC2002 database at ∼ 0 % FAR the GAR observed is 75 % and at ∼ 18 % FAR, GAR is 93 % . Proposed algorithm yields better GAR at low FAR with reduced computational complexity. Because of simplicity in computations the algorithm can be easily implemented as an embedded automatic fingerprint identification system (AFIS).

Speed-up of Fingerprint Verification Using Similarity of Fingerprint Spectrums

On the fingerprint images, ridgelines and ravines are continuous. Therefore, one-dimensional spectrums calculated along the lines on fingerprint images are similar in the neighborhood. In this paper, we propose the way to speed-up fingerprint verification algorithm based on group delay spectrum analysis by using similarity of fingerprint spectrums.