A brief introduction into biometrics is described in this thesis leading to description and to design a solution of verification system using speech analysis. The designed system provides firstly basic signal processing, then vowel recognition in fluent Czech speech. For each found vowel, observed speech features are calculated. The created GUI application was tested on created speaker database and its efficiency is approximately 54 % for short testing utterances, and approx. 88 % for long testing utterances respectively.

The aim of the thesis is to provide complete information regarding electronic documents and possibilities of their usage. The focus is concentrated on the area of authentication, which specifies the possibility of obtaining authentication information and describes the authentication processes itself. The diploma thesis also deals with the suggestion of multifactor authentication of electronic documents for the selected company.

This diploma thesis focuses on multibiometric systems, specifically on biometric fusion. The thesis describes eye biometrics, i.e. recognition based on retina and iris. The key part consists of design and implementation specification of a biometric system based on retina and iris recognition.

This work focuses on liveness detection for the fingerprint recognition technology. The first part of this thesis describes biometrics, biometric systems, liveness detection and the method for liveness detection is proposed, which is based on spectroscopic characteristics of human skin. The second part describes and summarizes performed experiments. In the end, the results are discussed and further improvements are proposed.

Tato disertační práce se zabývá biometrickým rozpoznáváním 3D obličejů. V úvodu práce jsou prezentovány současné metody a techniky pro rozpoznávání. Následně je navržen nový algoritmus, který využívá tzv. multialgoritmickou biometrickou fúzi. Vstupní snímek 3D obličeje je paralelně zpracování dílčími rozpoznávacími podalgoritmy a celkové rozhodnutí o identitě nebo verifikaci identity uživatele je výsledkem sloučení výstupu těchto podalgoritmů. Rozpoznávací algoritmus byl testován na veřejně přístupné databázi 3D obličejů FRGC v 2.0 i vlastních databázich, které byly pořízeny pomocí senzorů Microsoft Kinect a SoftKinetic DS325.

Projekt sa zaoberá spracovaním a porovnaním otlačkov prstov. Preberá obecné princípy biometrie a rôzne metódy analýzy otlačkov prstov. Navrhuje vlastné riešenie problému formou adaptívnych maskových operátorov na detekciu markantov a štatistické spracovanie výsledkov.

Le développement et la multiplication de dispositifs connectés, en particulier avec les \textit{smartphones}, nécessitent la mise en place de moyens d'authentification. Dans un soucis d'ergonomie, les industriels intègrent massivement des systèmes biométrique afin de garantir l'identité du porteur, et ce afin d'autoriser l'accès à certaines applications et fonctionnalités sensibles (paiements, e-banking, accès à des données personnelles : correspondance électronique..). Dans un soucis de garantir, une adéquation entre ces systèmes d'authentification et leur usages, la mise en œuvre d'un processus d'évaluation est nécessaire.L'amélioration des performances biométriques est un enjeux important afin de permettre l'intégration de telles solutions d'authentification dans certains environnement ayant d'importantes exigences sur les performances, particulièrement sécuritaires. Afin d'améliorer les performances et la fiabilité des authentifications, différentes sources biométriques sont susceptibles d'être utilisées dans un processus de fusion. La biométrie multimodale réalise, en particulier, la fusion des informations extraites de différentes modalités biométriques. / Development and spread of connected devices, in particular smartphones, requires the implementation of authentication methods. In an ergonomic concern, manufacturers integrates biometric systems in order to deal with logical control access issues. These biometric systems grant access to critical data and application (payment, e-banking, privcy concerns : emails...). Thus, evaluation processes allows to estimate the systems' suitabilty with these uses. In order to improve recognition performances, manufacturer are susceptible to perform multimodal fusion.In this thesis, the evaluation of operationnal biometric systems has been studied, and an implementation is presented. A second contribution studies the quality estimation of speech samples, in order to predict recognition performances.

This thesis analyzes what acoustically sets apart recordings of healthy people from recordings of people afflicted with multiple sclerosis, and how this distinction can be used to automatically detect multiple sclerosis from fairly simple recordings of a subject's voice, potentially discovering early cases of this disease. Chapter 1 includes the theoretical background of the effect of multiple sclerosis on speech and the descriptions of the data, software, hypotheses and assumptions used here. Two sets recordings of read speech were used, a corpus of afflicted speakers and a control corpus of healthy speakers, totalling 250 individuals. A subset of this corpus was manually annotated, resulting in one dataset. Simultaneously, these entire corpora were also annotated automatically, resulting in another dataset, which was created to explore the possibility of detecting multiple sclerosis automatically. Chapter 2 describes the 13 acoustic parameters used in this thesis, their exact hypothesized relationships with the symptoms of multiple sclerosis and the ways they were calculated. Chapter 3 elaborates on the statistical testing of the aforementioned parameters, their interpretation, the success rate of the two machine learning models used to assess their total predictive power, and a potential way to apply the...

Touchscreen biometrics is the process of measuring user behavior when using a touchscreen, and using this information for authentication. This thesis uses SVM and k-NN classifiers to test the applicability of touchscreen biometrics in a web environment for smartphones. Two new concepts are introduced: model training using the Local Outlier Factor (LOF), as well as building custom models for touch behaviour in the context of individual UI components instead of the whole screen. The lowest error rate achieved was 5.6 \% using the k-NN classifier, with a standard deviation of 2.29 \%. No real benefit using the LOF algorithm in the way presented in this thesis could be found. It is found that the method of using contextual models yields better performance than looking at the entire screen. Lastly, ideas for using touchscreen biometrics as an intrusion detection system is presented. / Pekskärmsbiometri innebär att mäta beteende hos en användare som använder en pekskärm och känna denna baserat på informationen. I detta examensarbete används SVM och k-NN klassifierare för att testa tillämpligheten av denna typ av biometri i en webbmiljö för smarttelefoner. Två nya koncept introduceras: modellträning med ''Local Outlier Factor'' samt att bygga modeller för användarinteraktioner med enskilda gränssnittselement iställer för skärmen i sin helhet. De besta resultaten för klassifierarna hade en felfrekvens på 5.6 \% med en standardavvikelse på 2.29 \%. Ingen fördel med användning av LOF för träning framför slumpmässig träning kunde hittas. Däremot förbättrades resultaten genom att använda kontextuella modeller. Avslutande så presenteras idéer för hur ett system som beskrivet kan användas för att upptäcka intrång i webbsystem.

<p>The increase in the use of digital devices, has vastly increased the amount of data used and consequently, has increased the availability and relevance of digital evidence. Typically, digital evidence helps to establish the identity of an offender by identifying the username or the user account logged into the device at the time of offense. Investigating officers need to establish the link between that user and an actual person. This is difficult in the case of computers that are shared or compromised. Also, the increasing amount of data in digital investigations necessitates the use of advanced data analysis approaches like machine learning, while keeping pace with the constantly evolving techniques. It also requires reporting on known error rates for these advanced techniques. There have been several research studies exploring the use of behavioral biometrics to support this user attribution in digital forensics. However, the use of the state-of-the-art XGBoost algorithm, hasn’t been explored yet. This study builds on previously conducted research by modeling user interaction using the XGBoost algorithm, based on features related to keystroke and mouse usage, and verifying the performance for user attribution. With an F1 score and Area Under the Receiver Operating Curve (AUROC) of .95, the algorithm successfully attributes the user event to the right user. The XGBoost model also outperforms other classifiers based on algorithms such as Support Vector Machines (SVM), Boosted SVM and Random Forest.</p>