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

A primer on computational statistics for ordinal models with applications to survey data

The analysis of survey data is a frequently arising issue in clinical trials, particularly when capturing quantities which are difficult to measure using, e.g., a technical device or a biochemical procedure. Typical examples are questionnaires about patient's well-being, pain, anxiety, quality of life or consent to an intervention. Data is captured on a discrete scale containing only a limited (usually three to ten) number of possible answers, of which the respondent has to pick the answer which fits best his personal opinion to the question. This data is generally located on an ordinal scale as answers can usually be arranged in an increasing order, e.g., "bad", "neutral", "good" for well-being or "none", "mild", "moderate", "severe" for pain. Since responses are often stored numerically for data processing purposes, analysis of survey data using ordinary linear regression (OLR) models seems to be natural. However, OLR assumptions are often not met as linear regression requires a constant variability of the response variable and can yield predictions out of the range of response categories. Moreover, in doing so, one only gains insights about the mean response which might, depending on the response distribution, not be very representative. In contrast, ordinal regression models are able to provide probability estimates for all response categories and thus yield information about the full response scale rather than just the mean. Although these methods are well described in the literature, they seem to be rarely applied to biomedical or survey data. In this paper, we give a concise overview about fundamentals of ordinal models, applications to a real data set, outline usage of state-of-the-art-software to do so and point out strengths, limitations and typical pitfalls. This article is a companion work to a current vignette-based structured interview study in paediatric anaesthesia

Non-asymptotic confidence sets for circular means

The mean of data on the unit circle is defined as the minimizer of the average squared Euclidean distance to the data. Based on Hoeffding’s mass concentration inequalities, non-asymptotic confidence sets for circular means are constructed which are universal in the sense that they require no distributional assumptions. These are then compared with asymptotic confidence sets in simulations and for a real data set

Internet-delivered cognitive behavioural therapy programme to reduce depressive symptoms in patients with multiple sclerosis: a multicentre, randomised, controlled, phase 3 trial

BACKGROUND: Depression is three to four times more prevalent in patients with neurological and inflammatory disorders than in the general population. For example, in patients with multiple sclerosis, the 12-month prevalence of major depressive disorder is around 25% and it is associated with a lower quality of life, faster disease progression, and higher morbidity and mortality. Despite its clinical relevance, there are few treatment options for depression associated with multiple sclerosis and confirmatory trials are scarce. We aimed to evaluate the safety and efficacy of a multiple sclerosis-specific, internet-based cognitive behavioural therapy (iCBT) programme for the treatment of depressive symptoms associated with the disease. METHODS: This parallel-group, randomised, controlled, phase 3 trial of an iCBT programme to reduce depressive symptoms in patients with multiple sclerosis was carried out at five academic centres with large outpatient care units in Germany and the USA. Patients with a neurologist-confirmed diagnosis of multiple sclerosis and depressive symptoms were randomly assigned (1:1:1; automated assignment, concealed allocation, no stratification, no blocking) to receive treatment as usual plus one of two versions of the iCBT programme Amiria (stand-alone or therapist-guided) or to a control condition, in which participants received treatment as usual and were offered access to the iCBT programme after 6 months. Masking of participants to group assignment between active treatment and control was not possible, although raters were masked to group assignment. The predefined primary endpoint, which was analysed in the intention-to-treat population, was severity of depressive symptoms as measured by the Beck Depression Inventory-II (BDI-II) at week 12 after randomisation. This trial is registered at ClinicalTrials.gov, NCT02740361, and is complete. FINDINGS: Between May 3, 2017, and Nov 4, 2020, we screened 485 patients for eligibility. 279 participants were enrolled, of whom 101 were allocated to receive stand-alone iCBT, 85 to receive guided iCBT, and 93 to the control condition. The dropout rate at week 12 was 18% (50 participants). Both versions of the iCBT programme significantly reduced depressive symptoms compared with the control group (BDI-II between-group mean differences: control vs stand-alone iCBT 6·32 points [95% CI 3·37-9·27], p<0·0001, effect size d=0·97 [95% CI 0·64-1·30]; control vs guided iCBT 5·80 points [2·71-8·88], p<0·0001, effect size d=0·96 [0·62-1·30]). Clinically relevant worsening of depressive symptoms was observed in three participants in the control group, one in the stand-alone iCBT group, and none in the guided iCBT group. No occurrences of suicidality were observed during the trial and there were no deaths. INTERPRETATION: This trial provides evidence for the safety and efficacy of a multiple sclerosis-specific iCBT tool to reduce depressive symptoms in patients with the disease. This remote-access, scalable intervention increases the therapeutic options in this patient group and could help to overcome treatment barriers

Film Unions’ Struggle to Defend Studio Space in Toronto

This chapter looks at the tension that emerged in Toronto, where film workers sharing local space with lower-income residents found themselves positioned as gentrifiers in their campaign to save studio space from being redeveloped for big-box retail. It considers the urban planning dimension of labor strategy in a case in which local film unions mobilized to defend low-cost studio space at risk of being lost as an unintended consequence of the city's real estate-led policies. This shows the contradictions of the luxury city strategy for labor in a high-skill/high-wage sector. While labor's campaign has so far proven successful in securing employment in this particular case, the campaign reinforced a competitive creative city discourse that is at odds with industrial employment retention efforts in a rapidly gentrifying city.</p