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

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

Online Visual Robot Tracking and Identification using Deep LSTM Networks

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar

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

Online signature verification techniques

Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. Signature verification is split into two according to the available data in the input. Offline (static) signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. Online (dynamic) signature verification uses signatures that are captured by pressure-sensitive tablets that extract dynamic properties of a signature in addition to its shape. The purpose of project is to develop an authentication system based on personal signatures. Signature verification is an important research topic in the area of biometric authentication. In this project the work is done in such a way that the signatures are captured using WEBCAM. A visual-based online signature verification system in which the signer’s pen tip is tracked. The data acquisition of the system consists of only low-cost cameras (webcams) and does not need special equipment such as an electronic tablet. Online signature data is obtained from the images captured by the webcams by tracking the pen tip. The pen tip tracking is implemented by the Sequential Monte Carlo method in real time. Then, the distance between the input signature data and reference signature data enrolled in advance is computed using Dynamic Time Warping (DTW). Finally, the input signature is classified as genuine or a forgery by comparing the distance with a threshold

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Interpretable Probabilistic Password Strength Meters via Deep Learning

Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a clear probabilistic interpretation. In our contribution: (1) we formulate the theoretical foundations of interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.Comment: An abridged version of this paper appears in the proceedings of the 25th European Symposium on Research in Computer Security (ESORICS) 202

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty

 Generative Adversarial Networks (GANs), as most popular artificial intelligence models in the current image generation field, have excellent image generation capabilities. Based on Wasserstein GANs with gradient penalty, this paper proposes a novel digital core reconstruction method. First, a convolutional neural network is used as a generative network to learn the distribution of real shale samples, and then a convolutional neural network is constructed as a discriminative network to distinguish reconstructed shale samples from real ones. Through this confrontation training method, realistic digital core samples of shale can be reconstructed. The paper uses two-point covariance function, Frechet Inception Distance and Kernel Inception Distance, to evaluate the quality of digital core samples of shale reconstructed by GANs. The results show that the covariance function can test the similarity between generated and real shale samples, and that GANs can efficiently reconstruct digital core samples of shale with high-quality. Compared with multiple point statistics, the new method does not require prior inference of the probability distribution of the training data, and directly uses noise vector to generate digital core samples of shale without using constraints of "hard data" in advance. It is easy to produce an unlimited number of new samples. Furthermore, the training time is also shorter, only 4 hours in this paper. Therefore, the new method has some good points compared with current methods.Cited as: Zha, W., Li, X., Xing, Y., He, L., Li, D. Reconstruction of shale image based on Wasserstein Generative Adversarial Networks with gradient penalty. Advances in Geo-Energy Research, 2020, 4(1): 107-114, doi: 10.26804/ager.2020.01.1

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