A Survey of the methods on fingerprint orientation field estimation

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): Accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

An orientation field approach to modelling fibre-generated spatial point processes

This thesis introduces a new approach to analysing spatial point data clustered along or around a system of curves or fibres with additional background noise. Such data arise in catalogues of galaxy locations, recorded locations of earthquakes, aerial images of minefields, and pore patterns on fingerprints. Finding the underlying curvilinear structure of these point-pattern data sets may not only facilitate a better understanding of how they arise but also aid reconstruction of missing data. We base the space of fibres on the set of integral lines of an orientation field. Using an empirical Bayes approach, we estimate the field of orientations from anisotropic features of the data. The orientation field estimation draws on ideas from tensor field theory (an area recently motivated by the study of magnetic resonance imaging scans), using symmetric positive-definite matrices to estimate local anisotropies in the point pattern through the tensor method. We also propose a new measure of anisotropy, the modified square Fractional Anisotropy, whose statistical properties are estimated for tensors calculated via the tensor method. A continuous-time Markov chain Monte Carlo algorithm is used to draw samples from the posterior distribution of fibres, exploring models with different numbers of clusters, and fitting fibres to the clusters as it proceeds. The Bayesian approach permits inference on various properties of the clusters and associated fibres, and the resulting algorithm performs well on a number of very different curvilinear structures

A bisector line field approach to interpolation of orientation fields

We propose an approach to the problem of global reconstruction of an orientation field. The method is based on a geometric model called "bisector line fields", which maps a pair of vector fields to an orientation field, effectively generalizing the notion of doubling phase vector fields. Endowed with a well chosen energy minimization problem, we provide a polynomial interpolation of a target orientation field while bypassing the doubling phase step. The procedure is then illustrated with examples from fingerprint analysis

Characteristic and necessary minutiae in fingerprints

Fingerprints feature a ridge pattern with moderately varying ridge frequency (RF), following an orientation field (OF), which usually features some singularities. Additionally at some points, called minutiae, ridge lines end or fork and this point pattern is usually used for fingerprint identification and authentication. Whenever the OF features divergent ridge lines (e.g., near singularities), a nearly constant RF necessitates the generation of more ridge lines, originating at minutiae. We call these the necessary minutiae. It turns out that fingerprints feature additional minutiae which occur at rather arbitrary locations. We call these the random minutiae or, since they may convey fingerprint individuality beyond the OF, the characteristic minutiae. In consequence, the minutiae point pattern is assumed to be a realization of the superposition of two stochastic point processes: a Strauss point process (whose activity function is given by the divergence field) with an additional hard core, and a homogeneous Poisson point process, modelling the necessary and the characteristic minutiae, respectively. We perform Bayesian inference using an Markov-Chain-Monte-Carlo (MCMC)-based minutiae separating algorithm (MiSeal). In simulations, it provides good mixing and good estimation of underlying parameters. In application to fingerprints, we can separate the two minutiae patterns and verify by example of two different prints with similar OF that characteristic minutiae convey fingerprint individuality

Characteristic and necessary minutiae in fingerprints

Fingerabdrücke sind Abbilder der Papillarlinien, welche ein ungerichtetes Orientierungsfeld (OF) induzieren. Dieses weist in der Regel einige Singularitäten auf. Die Linien variieren in ihrer Breite und induzieren so eine mäßig variierende Linienfrequenz (LF). Bei der Fingerabdruckserkennung wird ein Fingerabdruck üblicherweise auf ein Punktmuster reduziert, das aus Minutien besteht, das sind Punkte, an denen die Papillarlinien enden oder sich verzweigen. Geometrisch können Minutien durch divergierende Papillarlinien bei nahezu konstanter LF oder bei nahezu parallelen Linien durch Verbreiterung der Zwischenräume entstehen, in welchen neue Linien entstehen, welche in Minutien entspringen (und natürlich Kombinationen aus beiden Effekten). Wir nennen diese die geometrisch notwendigen Minutien. In dieser Arbeit stellen wir ein mathematisches Rahmenkonzept basierend auf Vektorfeldern bereit, in dem Orientierungsfelder, Linienfrequenz sowie die Anzahl der geometrisch notwendigen Minutien mathematisch konkret und leicht mit den bereitgestellten Algorithmen und dazugehöriger Software berechenbar werden. Es stellt sich heraus, dass echte Fingerabdrücke zusätzliche Minutien aufweisen, die an recht zufälligen Stellen auftreten. Wir nennen diese die zufälligen Minutien oder, da sie zur Fingerabdrucksindividualität über OF und LF hinaus beitragen können, die charakteristischen Minutien. In der Folge wird angenommen, dass ein Minutien-Punktmuster eine Realisierung der Überlagerung zweier stochastischer Punktprozesse ist: einem Strauss-Punktprozess (dessen Aktivitätsfunktion durch das Divergenzfeld gegeben ist) mit einem zusätzlichen Hard-core und einem homogenen Poisson-Punktprozess, welche die notwendigen bzw. die charakteristischen Minutien modellieren. Für ein gegebenes Minutienmuster streben wir nach einer Methode, die sowohl die Separation der Minutien als auch Inferenz für die Modellparameter ermöglicht. Wir betrachten das Problem aus zwei Perspektiven. Aus frequentistischer Sicht betrachten wir zunächst lediglich die Schätzung der Modellparameter (ohne Trennung der Prozesse). Dazu legen wir die Grundlagen für parametrische Inferenz, indem wir die Dichte des überlagerten Prozesses herleiten und ein Identifizierbarkeitsergebnis liefern. Wir schlagen einen Ansatz zur Berechnung eines Maximum-Pseudolikelihood-Schätzers vor und zeigen Vor- und Nachteile dieses Schätzers für echte und simulierte Daten auf. Einem Bayesianischen Ansatz folgend, schlagen wir einen MCMC-basierten Minutien-Separationsalgorithmus (MiSeal) vor, der es ermöglicht, die zugrunde liegenden Modellparameter sowie die Posterior-Wahrscheinlichkeiten von Minutien charakteristisch zu sein zu schätzen. Für zwei verschiedene Fingerabdrücke mit ähnlichen OF und LF weisen wir empirisch nach, dass die charakteristischen Minutien tatsächlich individuelle Fingerabdrucksinformation beinhalten.Fingerprints feature a ridge line pattern inducing an undirected orientation field (OF) which usually features some singularities. Ridges vary in width, inducing a moderately varying ridge frequency (RF). In fingerprint recognition, a fingerprint is usually reduced to a point pattern consisting of minutiae, i.e. points where the ridge lines end or fork. Geometrically, minutiae can occur due to diverging ridge lines with a nearly constant RF or by widening of parallel ridges making space for new ridge lines originating at minutiae (and, indeed, combinations of both). We call these the geometrically necessary minutiae. In this thesis, we provide a mathematical framework based on vector fields in which orientation fields, ridge frequency as well as the number of geometrically necessary minutiae become tangible and easily computable using the provided algorithms and software. It turns out that fingerprints feature additional minutiae which occur at rather arbitrary locations. We call these the random minutiae, or, since they may convey fingerprint individuality beyond OF and RF, the characteristic minutiae. In consequence, a minutiae point pattern is assumed to be a realization of the superposition of two stochastic point processes: a Strauss point process (whose activity function is given by the divergence field) with an additional hard core, and a homogeneous Poisson point process, modelling the necessary and the characteristic minutiae, respectively. Given a minutiae pattern we strive for a method allowing for separation of minutiae and inference for the model parameters and consider the problem from two view points. From a frequentist point of view we first solely aim on estimating the model parameters (without separating the processes). To this end, we lay the foundations for parametric inference by deriving the density of the superimposed process and provide an identifiability result. We propose an approach for the computation of a maximum pseudolikelihood estimator and highlight benefits and drawbacks of this estimator on real and simulated data. Following a Bayesian approach we propose an MCMC-based minutiae separating algorithm (MiSeal) which allows for estimation of the underlying model parameters as well as of the posterior probabilities of minutiae being characteristic. In a proof of concept, we provide evidence that for two different prints with similar OF and RF the characteristic minutiae convey fingerprint individuality.2021-10-2

An orientation field approach to modelling fibre-generated spatial point processes

This thesis introduces a new approach to analysing spatial point data clustered along or around a system of curves or fibres with additional background noise. Such data arise in catalogues of galaxy locations, recorded locations of earthquakes, aerial images of minefields, and pore patterns on fingerprints. Finding the underlying curvilinear structure of these point-pattern data sets may not only facilitate a better understanding of how they arise but also aid reconstruction of missing data. We base the space of fibres on the set of integral lines of an orientation field. Using an empirical Bayes approach, we estimate the field of orientations from anisotropic features of the data. The orientation field estimation draws on ideas from tensor field theory (an area recently motivated by the study of magnetic resonance imaging scans), using symmetric positive-definite matrices to estimate local anisotropies in the point pattern through the tensor method. We also propose a new measure of anisotropy, the modified square Fractional Anisotropy, whose statistical properties are estimated for tensors calculated via the tensor method. A continuous-time Markov chain Monte Carlo algorithm is used to draw samples from the posterior distribution of fibres, exploring models with different numbers of clusters, and fitting fibres to the clusters as it proceeds. The Bayesian approach permits inference on various properties of the clusters and associated fibres, and the resulting algorithm performs well on a number of very different curvilinear structures.EThOS - Electronic Theses Online ServiceAarhus universitet. Matematisk institutGBUnited Kingdo

An orientation field approach to modelling fibre-generated spatial point processes

This thesis introduces a new approach to analysing spatial point data clustered along or around a system of curves or fibres with additional background noise. Such data arise in catalogues of galaxy locations, recorded locations of earthquakes, aerial images of minefields, and pore patterns on fingerprints. Finding the underlying curvilinear structure of these point-pattern data sets may not only facilitate a better understanding of how they arise but also aid reconstruction of missing data. We base the space of fibres on the set of integral lines of an orientation field. Using an empirical Bayes approach, we estimate the field of orientations from anisotropic features of the data. The orientation field estimation draws on ideas from tensor field theory (an area recently motivated by the study of magnetic resonance imaging scans), using symmetric positive-definite matrices to estimate local anisotropies in the point pattern through the tensor method. We also propose a new measure of anisotropy, the modified square Fractional Anisotropy, whose statistical properties are estimated for tensors calculated via the tensor method. A continuous-time Markov chain Monte Carlo algorithm is used to draw samples from the posterior distribution of fibres, exploring models with different numbers of clusters, and fitting fibres to the clusters as it proceeds. The Bayesian approach permits inference on various properties of the clusters and associated fibres, and the resulting algorithm performs well on a number of very different curvilinear structures.EThOS - Electronic Theses Online ServiceAarhus universitet. Matematisk institutGBUnited Kingdo