Automatic Fingerprint Identification Research Articles

Fingerprint Singular Point Detection Using Orientation Field Reliability

Singular point detection is the most important step in Automatic Fingerprint Identification System (AFIS) and is used in fingerprint alignment, fingerprint matching, and particularly in classification. The computation of orientation field of a fingerprint can be verified by computing orientation field reliability. The most unreliable portion in orientation field can be the possible location of singular points. In this paper we have proposed a novel algorithm for detecting singular points using reliability of the fingerprint orientation field. Experimental results show that the proposed algorithm accurately detects singular points (core and delta) with the detection rate of 92.6 %.

Research of Automatic Fingerprint Recognition Essential Algorithms

Fingerprint recognition has wide application prospect in all fields which contain identity authentication. Construction of accurate and reliable，safe and Practical automatic fingerprint identification system(AFIS) has become researc hotspot. Although theoretical research and application developmen of AFIS have got a significant Progress，accuracy of the algorithm and proeessing speeds till need to be improved. In this paper, fingerprint image preprocessing algorithms，fingerprint singular Points and minutiae extraction algorithm and fingerprint matching algorithm are analyzed and discussed in detail.

Design of Embedded Automated Fingerprint Identification System Based on DSP

The automated fingerprint identification algorithm has high time and space complexity in the embedded system. How to reduce the complexity is one of the hot research topics. The process of fingerprint identification and choice of algorithm platform are analyzed in the paper. Design of embedded fingerprint identification hardware system based on DSP, including the selection of microprocessor and fingerprint sensor and the communication between them, is introduced in detail. In additional, main software composition and flow are explained. At last, serial peripheral interface communication is simulated.

A Quantitative Survey of various Fingerprint Enhancement techniques

Preprocessing is an important step in the area of image processing and pattern recognition. This paper aims to present a review of recent as well as classic fingerprint image enhancement techniques. The umbrella of techniques used for evaluation varies from histogram based enhancement, frequency transformation based, Gabor filter based enhancement and its variants to composite enhancement technique. The effectiveness of enhancement techniques proposed by various researchers is evaluated on the basis of peak signal to noise ratio and equal error rate which refers to robustness and stability of identification process. Experimental results shows that incorporating the enhancement technique based on Gabor filter in wavelet domain and composite method improves equal error rate .Improved error rate and peak signal noise ratio improves the identification/verification accuracy marginally. The major goal of the paper is to provide a comprehensive reference source for the researchers involved in enhancement of fingerprint images which is essential preprocessing step in automatic fingerprint identification and verification.

Two-Dimensional Sine Filter Fingerprint Enhancement Algorithm Based on Block Level

Enhancing low-quality fingerprint image is the effective method for improving the accuracy of minutia extraction and performance of the automatic fingerprint identification system. Fingerprint image is one kind of regular texture image in nature. To design two-dimensional sinusoidal surface model which accorded with the ridge gray distribution rule and propose the fingerprint image enhancement algorithm based on the two-dimensional sinusoidal surface model after analyzing the basic character of the fingerprint image. The experimental results indicate that the fingerprint image enhancement algorithm has better connecting ability for the broken ridges than the Gabor fingerprint enhancement algorithm. The algorithm can improve effectively the fingerprint image enhancement result and the accuracy of the minutiae extraction.

FINGERPRINT MATCHING BASED ON PORE CENTROIDS

In recent years there has been exponential growth in the use of biometrics for user authentication applications. Automated Fingerprint Identification systems have become popular tool in many security and law enforcement applications. Most of these systems rely on minutiae (ridge ending and bifurcation) features. With the advancement in sensor technology, high resolution fingerprint images (1000 dpi) provide micro level of features (pores) that have proven to be useful features for identification. In this paper, we propose a new strategy for fingerprint matching based on pores by reliably extracting the pore features The extraction of pores is done by Marker Controlled Watershed segmentation method and the centroids of each pore are considered as feature vectors for matching of two fingerprint images. Experimental results shows that the proposed method has better performance with lower false rates and higher accuracy.

Accuracy and reliability of forensic latent fingerprint decisions

The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. The National Research Council of the National Academies and the legal and forensic sciences communities have called for research to measure the accuracy and reliability of latent print examiners' decisions, a challenging and complex problem in need of systematic analysis. Our research is focused on the development of empirical approaches to studying this problem. Here, we report on the first large-scale study of the accuracy and reliability of latent print examiners' decisions, in which 169 latent print examiners each compared approximately 100 pairs of latent and exemplar fingerprints from a pool of 744 pairs. The fingerprints were selected to include a range of attributes and quality encountered in forensic casework, and to be comparable to searches of an automated fingerprint identification system containing more than 58 million subjects. This study evaluated examiners on key decision points in the fingerprint examination process; procedures used operationally include additional safeguards designed to minimize errors. Five examiners made false positive errors for an overall false positive rate of 0.1%. Eighty-five percent of examiners made at least one false negative error for an overall false negative rate of 7.5%. Independent examination of the same comparisons by different participants (analogous to blind verification) was found to detect all false positive errors and the majority of false negative errors in this study. Examiners frequently differed on whether fingerprints were suitable for reaching a conclusion.

Fingerprint identification on a bog body (650 BC)

Two thousand and six hundred fifty years after the death of the “Girl of the Uchter Moor”, it was still possible to secure good-quality fingerprints from the right hand of this bog body by using photographic methods followed by image enhancement with an Automated Fingerprint Identification System. A detailed fingerprint analysis was performed, and all five fingers of “Moora's” right hand exhibited ulnar loops. Even today, this is still the most common fingerprint pattern type in Europe. It was also possible to find enough minutiae on the right thumb for secure dactyloscopic identification after more than 2,000 years. The example of “Moora" therefore demonstrates new possibilities for future fingerprint examinations of ancient bodies in archaeological as well as forensic casework.

Fingerprint segmentation based on an AdaBoost classifier

Fingerprint segmentation is one of the most important preprocessing steps in an automatic fingerprint identification system (AFIS). Accurate segmentation of a fingerprint will greatly reduce the computation time of the following processing steps, and the most importantly, exclude many spurious minutiae located at the boundary of foreground. In this paper, a new fingerprint segmentation algorithm is presented. First, two new features, block entropy and block gradient entropy, are proposed. Then, an AdaBoost classifier is designed to discriminate between foreground and background blocks based on these two features and five other commonly used features. The classification error rate (Err) and McNemar's test are used to evaluate the performance of our method. Experimental results on FVC2000, FVC2002 and FVC2004 show that our method outperforms other methods proposed in the literature both in accuracy and stability.

Classification-based Automatic Fingerprint Identification System for Large Distributed Fingerprint Database

Fingerprint is the cheapest, fastest, most convenient and most reliable way to identify someone. And the tendency, due to scale, easiness and the existing foundation, is that the use of fingerprint will only increase. Cars, cell phones, PDAs, personal computers and dozens of products and devices are using fingerprints more and more. When it comes to deal with large-scale fingerprint database, the scalability of current fingerprint recognition system is not proved yet. Our target application is a centralized user authentication system for e-commerce including online shopping malls and for user identification for government web services. In this paper, we introduce a large-scale fingerprint recognition system which incorporates fingerprint classification, large-scale identification, multiple server based identification. Our experimental result shows that our multi-server system takes 5.2 - 5.36 seconds to identify a fingerprint from 100,000 fingerprints.

A topological interpretation of fingerprint reference point

AbstractA critical issue in the development of an automated fingerprint identification system (AFIS) is to detect the reference point accurately and reliably. Most existing techniques are based on the singular points of the fingerprint which is sensitive to fingerprint artifacts and is not well defined for arch type fingerprints. In this study, we present a topological interpretation of fingerprint reference point, where the reference points are posed as topological features of fingerprint structure and can be detected seamlessly from either arch type or non‐arch type fingerprints. Extensive experiments demonstrate the advantages of the proposed method over the existing ones. Copyright © 2010 John Wiley & Sons, Ltd.

Transplanted Fingerprints: A Preliminary Case Report 40 Months Posttransplant

For the past century, fingerprints have been considered permanent and specific for each individual. However, with the advances in transplantology, fingerprints have lost their permanence. Because no study has yet been described, we examined possible changes in the fingerprint pattern of a transplanted hand. In 2006, we performed a hand transplantation on a 32-year-old man. The donor was revealed to have had a criminal record; his fingerprints were stored in the Polish automated fingerprint identification system. A forensic technician fingerprinted the transplanted hand nine times between June 2006 and September 2009. The appearance of minutiae and white lines and the change in the distance between papillary ridges were assessed in the thumbprints of the transplanted hand. The appearance of white lines was only temporary; at no point did they impair fingerprint identification. No significant changes occurred in the distance between the friction ridges. The observed small differences were ascribed to the two techniques used to collect the prints (spoon vs rolling). The number of minutiae ranged from 1 to 3, reaching a maximum in the third posttransplant month. A 40-month observation showed no significant changes in the fingerprints of the transplanted hand. Nevertheless, a long-term study is needed because of the risk of chronic rejection. The noninvasiveness of dactylography argues for inspecting its application to diagnose acute rejection. Finally, lawmakers should be made aware of the personal-protection issues related to the growing number of hand-transplant recipients.

Fingerprint Identification using Sectorized Cepstrum Complex Plane

Using biometrics to verify a person’s identity has several advantages over the present practices of personal identification numbers (PINs) and passwords. Minutiae-based automated fingerprint identification systems are more popular, but they are more computationally complex and time consuming. In this paper we propose a simple yet effective technique for fingerprint identification. This method is image-based in which feature vectors of a fingerprint are extracted after sectorization of the cepstrum of a fingerprint. They are matched with those stored in the database. The experimental results show that this algorithm could correctly identify fingerprints with accuracy more than 96% in case of larger number of sectors.

Optical sensor measurement and biometric-based fractal pattern classifier for fingerprint recognition

This paper proposes biometric-based fractal pattern classifier for fingerprint recognition using grey relational analysis (GRA). Fingerprint patterns have arch, loop, whorl, and accidental morphologys, and embed singular points, which result in establishing fingerprint individuality. An automatic fingerprint identification system consists of three stages: image acquisition and processing, feature extraction, and pattern recognition. Fingerprint images are captured from subjects using an optical fingerprint reader (OFR). Digital image preprocessing (DIP) is used to refine out noise, enhance the image, convert to binary image, and locate the reference point. For binary images, Katz’s algorithm is employed to estimate the fractal dimension (FD) from two-dimension (2D) image. Biometric characteristics are extracted as fractal patterns using Weierstrass cosine function (WCF) with different FDs. GRA performs to compare the fractal patterns among the small-scale database. For 30 subjects in the laboratory, the proposed classifier demonstrates greater efficiency and higher accuracy in fingerprint recognition.

Magnetic Fingerprint Powder from a Mineral Indigenous to Thailand

A study was conducted to investigate whether natural magnetite (Fe3O4), which is an abundant mineral in Thailand, could be used as a magnetic powder in the detection of latent fingerprints. Because of the presence of impurities, powdered magnetite is only weakly attracted by a magnet and cannot be used as a magnetic fingerprint powder by itself. Mixing a small amount of magnetite powder with nickel powder greatly enhances the magnetic attraction. A mixture of magnetite powder and nickel powder in a mass ratio of approximately 1:100 was found to be suitable for use as a magnetic fingerprint powder. Fingerprints developed using the magnetite/nickel mixture on nonporous surfaces were found to exhibit good adherence and clarity. Using an automated fingerprint identification system, the number of minutiae detected in fingerprints developed by using the prepared powder on nonporous surfaces was found to be comparable to those detected in fingerprints developed by using a commercial black magnetic powder. The cost is lowered by more than 60%.

Modeling the Growth of Fingerprints Improves Matching for Adolescents

We study the effect of growth on the fingerprints of adolescents, based on which we suggest a simple method to adjust for growth when trying to recover a juvenile's fingerprint in a database years later. Based on longitudinal data sets in juveniles' criminal records, we show that growth essentially leads to an isotropic rescaling, so that we can use the strong correlation between growth in stature and limbs to model the growth of fingerprints proportional to stature growth as documented in growth charts. The proposed rescaling leads to a 72% reduction of the distances between corresponding minutiae for the data set analyzed. These findings were corroborated by several verification tests. In an identification test on a database containing 3.25 million right index fingers at the Federal Criminal Police Office of Germany, the identification error rate of 20.8% was reduced to 2.1% by rescaling. The presented method is of striking simplicity and can easily be integrated into existing automated fingerprint identification systems.

Resource Description Framework Schemas for Fingerprint Image

The aim of this paper is to describe the fingerprint image using metadata. Fingerprint image was using in Automated Fingerprint Identification (AFI) system to recognize the person. There are many difficulties in using this system. AFI system is a central system in addition to that there are no metadata for fingerprint image. Describe the fingerprint using metadata and Resource Description Framework (RDF) embedded in fingerprint image should make it more flexible to detecting of individuals wanted at any times using metadata. This paper led to new method to retrieve the person by using metadata instead of matching fingerprint image.

Quality and matching performance analysis of three-dimensional unraveled fingerprints

The use of fingerprints as a biometric is both the oldest mode of computer-aided personal identification and the most-relied-on technology in use today. However, current acquisition methods have some challenging and peculiar difficulties. For higher performance fingerprint data acquisition and verification, a novel noncontact 3-D fingerprint scanner is investigated, where both the detailed 3-D and albedo information of the finger is obtained. The obtained high-resolution 3-D prints are further converted into 3-D unraveled prints, to be compatible with traditional 2-D automatic fingerprint identification systems. As a result, many limitations imposed on conventional fingerprint capture and processing can be reduced by the unobtrusiveness of this approach and the extra depth information acquired. To compare the quality and matching performances of 3-D unraveled with traditional 2-D plain fingerprints, we collect both 3-D prints and their 2-D plain counterparts. The print quality and matching performances are evaluated and analyzed by using National Institute of Standard Technology fingerprint software. Experimental results show that the 3-D unraveled print outperforms the 2-D print in both quality and matching performances.

Fingerprint classification based on subclass analysis using multiple templates of support vector machines

Fingerprint classification reduces the searching time of an automated fingerprint identification system. Since fingerprints have properties of intra-class diversities and inter-class similarities, the ambiguous example causes a difficult problem in the fingerprint classification. In order to address the problem, we have analyzed fingerprints' subclasses with multiple decision templates. It clusters the soft outputs of support vector machines (SVMs) into several sub-classes using the self-organizing maps, and estimates a localized template for each sub-class. For an input fingerprint, the proposed method matches the output vector of SVMs to each template and finally categorizes the sample into the class of the most similar template. Experimental results on the FingerCode dataset demonstrate the effectiveness of the subclass-based approach compared with previous methods.

Implementation of a Fingerprint Recognition System Using LBP Descriptor

Abstract Conventional fingerprint recognition systems provide authentication by a direct matching of minutiae points and orientation field. Although several resemblance algorithms have been proposed, reliable automatic fingerprint verification remains a challenge due to the difficulty in alignment for direct matching and the construction of adequate functions for resemblance measurements. In this paper, we propose a solution to the aforementioned problems using a local binary pattern (LBP) descriptor applied to minutiae and orientation fields. The experimental results on the public fingerprint database, Fingerprint Verification Competition (FVC), show high recognition rates. The proposed system was implemented on the platform known as FPGA Virtex-II Xilinix™ (Virtex2p-xc2vp7-FF672) and optimized with respect to hardware resources occupation, based on a co-design methodology. All the proposed algorithms are involved in the design of a mixed software/hardware dedicated system. A classifier based on pulse mode neural networks using floating-point format is proposed.