Classification with class-independent quality information for biometric verification

Biometric identity verification systems frequently face the challenges of non-controlled conditions of data acquisition. Under such conditions biometric signals may suffer from quality degradation due to extraneous, identity-independent factors. It has been demonstrated in numerous reports that a degradation of biometric signal quality is a frequent cause of significant deterioration of classification performance, also in multiple-classifier, multimodal systems, which systematically outperform their single-classifier counterparts. Seeking to improve the robustness of classifiers to degraded data quality, researchers started to introduce measures of signal quality into the classification process. In the existing approaches, the role of class-independent quality information is governed by intuitive rather than mathematical notions, resulting in a clearly drawn distinction between the single-, multiple-classifier and multimodal approaches. The application of quality measures in a multiple-classifier system has received far more attention, with a dominant intuitive notion that a classifier that has data of higher quality at its disposal ought to be more credible than a classifier that operates on noisy signals. In the case of single-classifier systems a quality-based selection of models, classifiers or thresholds has been proposed. In both cases, quality measures have the function of meta-information which supervises but not intervenes with the actual classifier or classifiers employed to assign class labels to modality-specific and class-selective features. In this thesis we argue that in fact the very same mechanism governs the use of quality measures in single- and multi-classifier systems alike, and we present a quantitative rather than intuitive perspective on the role of quality measures in classification. We notice the fact that for a given set of classification features and their fixed marginal distributions, the class separation in the joint feature space changes with the statistical dependencies observed between the individual features. The same effect applies to a feature space in which some of the features are class-independent. Consequently, we demonstrate that the class separation can be improved by augmenting the feature space with class-independent quality information, provided that it sports statistical dependencies on the class-selective features. We discuss how to construct classifier-quality measure ensembles in which the dependence between classification scores and the quality features helps decrease classification errors below those obtained using the classification scores alone. We propose Q – stack, a novel theoretical framework of improving classification with class-independent quality measures based on the concept of classifier stacking. In the scheme of Q – stack a classifier ensemble is used in which the first classifier layer is made of the baseline unimodal classifiers, and the second, stacked classifier operates on features composed of the normalized similarity scores and the relevant quality measures. We present Q – stack as a generalized framework of classification with quality information and we argue that previously proposed methods of classification with quality measures are its special cases. Further in this thesis we address the problem of estimating probability of single classification errors. We propose to employ the subjective Bayesian interpretation of single event probability as credence in the correctness of single classification decisions. We propose to apply the credence-based error predictor as a functional extension of the proposed Q – stack framework, where a Bayesian stacked classifier is employed. As such, the proposed method of credence estimation and error prediction inherits the benefit of seamless incorporation of quality information in the process of credence estimation. We propose a set of objective evaluation criteria for credence estimates, and we discuss how the proposed method can be applied together with an appropriate repair strategy to reduce classification errors to a desired target level. Finally, we demonstrate the application of Q – stack and its functional extension to single error prediction on the task of biometric identity verification using face and fingerprint modalities, and their multimodal combinations, using a real biometric database. We show that the use of the classification and error prediction methods proposed in this thesis allows for a systematic reduction of the error rates below those of the baseline classifiers

Security in Voice Authentication

We evaluate the security of human voice password databases from an information theoretical point of view. More specifically, we provide a theoretical estimation on the amount of entropy in human voice when processed using the conventional GMM-UBM technologies and the MFCCs as the acoustic features. The theoretical estimation gives rise to a methodology for analyzing the security level in a corpus of human voice. That is, given a database containing speech signals, we provide a method for estimating the relative entropy (Kullback-Leibler divergence) of the database thereby establishing the security level of the speaker verification system. To demonstrate this, we analyze the YOHO database, a corpus of voice samples collected from 138 speakers and show that the amount of entropy extracted is less than 14-bits. We also present a practical attack that succeeds in impersonating the voice of any speaker within the corpus with a 98% success probability with as little as 9 trials. The attack will still succeed with a rate of 62.50% if 4 attempts are permitted. Further, based on the same attack rationale, we mount an attack on the ALIZE speaker verification system. We show through experimentation that the attacker can impersonate any user in the database of 69 people with about 25% success rate with only 5 trials. The success rate can achieve more than 50% by increasing the allowed authentication attempts to 20. Finally, when the practical attack is cast in terms of an entropy metric, we find that the theoretical entropy estimate almost perfectly predicts the success rate of the practical attack, giving further credence to the theoretical model and the associated entropy estimation technique

Decentralized nation, solving the web identity crisis

The web of today whether you prefer to call it web 2.0, web 3.0, web 5.0 or even the metaverse is at a critical stage of evolution and challenge, largely centered around its crisis of identity. Like teenagers who cannot assess properly their reason for being and do not seem ready to take responsibility for their actions, we are constantly blaming the very system we are trying to get away from. To truly realize the benefits from innovation and technology, this crisis has to be resolved, not just through tactical solutions but through developments that enhance the sustainability of the web and its benefits. Significant strides are being made in the evolution of digital services enabled by technology, regulation, and the sheer pace of societal change. The journey to the decentralized web is mirroring the convergence of the physical and digital worlds across all economies and is increasingly embracing the digital native world. Technology has provided the foundational platform for individuals and entities to create and manage wealth, potentially without the need for big institutions. Ironically, despite all of the advancements, we are still facing an unprecedented and increasing wealth gap. Clearly, the system is broken, not just around the edges but at the very core of the democratic underpinning of our society. In this whitepaper, we propose how artificial intelligence on blockchain can be used to generate a new class of identity through direct human computer interaction. We demonstrate how this, combined with new perspectives for sustaining community and governance embedded within the use of blockchain technology, will underpin a sustainable solution to protect identity, authorship and privacy at the same time while contributing to restore trust amongst members of a future decentralized nation and hence contribute to solving the web most significant identity crisis.Comment: 11 pages, 1 figur

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Insights From Behavioral Economics on Deviations From Rational Choice Theory

This dissertation combines different research fields to enrich understanding of economic phenomena by integrating stated preference methods, experimental economics, and marketing. Specifically, two laboratory experiments are designed and conducted to study how the number of alternatives available impacts market valuation studies, and how small changes in the experimental design impact honesty and trusting in markets with asymmetric information. Incentivizing has been proposed as a solution to the potential lack of candor in economic experiments, but how the number of alternatives available affects responses to incentives is uncertain. This question is explored using induced values in a discrete choice experiment, merging stated preference and experimental economics. Results indicate that engagement is positively correlated with profit-maximizing behavior, even after accounting for differences in payouts between alternatives. The number of alternatives available, however, does not affect profit-maximizing behavior when the difference between potential payouts is small, only when the difference between payouts is larger does profit-maximizing behavior improve. Results suggest that researchers can conduct incentivized choice experiments without all product alternatives available as long as participants are engaged with the task. The second experiment studies markets with information asymmetry. In particular, how manipulating seemingly trivial aspects of a decision process influence honesty and trusting in an asymmetric market. This is accomplished using a seller-buyer game, merging experimental economics and marketing. Results show dishonesty of sellers with asymmetric information is partially mitigated by the interaction of adding a probability of being caught lying, making truth the default option, and having self-control available. Results indicate that self-control depletion also reduces trusting in buyers. Engagement plays an important role, with increased engagement resulting in less trust by buyers and more honesty in sellers. A clearer picture of behavioral mechanisms in decision-making emerges by showing that engagement is more important than the number of alternatives available to promote profit-maximizing behavior. Further, the default option, self-control, and probability of being caught interact to affect honesty, and that engagement is crucial in economic decisions. This illustrates how behavioral economics contributes to solving economic problems in an multiple research area framework

How Technology Impacts and Compares to Humans in Socially Consequential Arenas

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the technology. However, these beneficial results do not apply in every scenario and may not impact everyone in a system the same way. Sometimes a technology is developed which produces both benefits and inflicts some harm. These harms may come at a higher cost to some people than others, raising the question: {\it how are benefits and harms weighed when deciding if and how a socially consequential technology gets developed?} The most natural way to answer this question, and in fact how people first approach it, is to compare the new technology to what used to exist. As such, in this work, I make comparative analyses between humans and machines in three scenarios and seek to understand how sentiment about a technology, performance of that technology, and the impacts of that technology combine to influence how one decides to answer my main research question.Comment: Doctoral thesis proposal. arXiv admin note: substantial text overlap with arXiv:2110.08396, arXiv:2108.12508, arXiv:2006.1262

Feature Selection and Classifier Development for Radio Frequency Device Identification

The proliferation of simple and low-cost devices, such as IEEE 802.15.4 ZigBee and Z-Wave, in Critical Infrastructure (CI) increases security concerns. Radio Frequency Distinct Native Attribute (RF-DNA) Fingerprinting facilitates biometric-like identification of electronic devices emissions from variances in device hardware. Developing reliable classifier models using RF-DNA fingerprints is thus important for device discrimination to enable reliable Device Classification (a one-to-many looks most like assessment) and Device ID Verification (a one-to-one looks how much like assessment). AFITs prior RF-DNA work focused on Multiple Discriminant Analysis/Maximum Likelihood (MDA/ML) and Generalized Relevance Learning Vector Quantized Improved (GRLVQI) classifiers. This work 1) introduces a new GRLVQI-Distance (GRLVQI-D) classifier that extends prior GRLVQI work by supporting alternative distance measures, 2) formalizes a framework for selecting competing distance measures for GRLVQI-D, 3) introducing response surface methods for optimizing GRLVQI and GRLVQI-D algorithm settings, 4) develops an MDA-based Loadings Fusion (MLF) Dimensional Reduction Analysis (DRA) method for improved classifier-based feature selection, 5) introduces the F-test as a DRA method for RF-DNA fingerprints, 6) provides a phenomenological understanding of test statistics and p-values, with KS-test and F-test statistic values being superior to p-values for DRA, and 7) introduces quantitative dimensionality assessment methods for DRA subset selection

Sistemi di autenticazione multibiometrica: strumenti a supporto della progettazione

Negli ultimi anni, l'autenticazione biometrica ha goduto di considerevoli miglioramenti, specialmente in materia di accuratezza ed affidabilità, con alcuni tratti che ottengono una buona performance complessiva. Tuttavia, persino i migliori tratti biometrici disponibili incontrano ancora numerosi problemi, alcuni dei quali inerenti alla tecnologia stessa. In particolare, i sistemi di autenticazione biometrica soffrono generalmente di problemi di enrollment dovuti alla non-universalità dei tratti, di suscettibilità allo spoofing biometrico oppure presentano un alto livello di inaccuratezza nelle misurazioni attribuibile per lo più ad acquisizioni rumorose dovute, nella maggior parte dei casi, all'ambiente. La multibiometria rappresenta un approccio che cerca di superare queste limitazioni, attraverso la realizzazione di un sistema che considera molteplici sorgenti di informazione biometrica. L'obiettivo di questa tesi è la realizzazione di un insieme di strumenti software che, prendendo in considerazione le molteplici scelte realizzative, supportano la progettazione di un sistema di autenticazione multibiometrico, nel contesto di un progetto più ampio che prevede anche la realizzazione di strumenti per il deployment dell'architettura di sistema e l'esecuzione della stess