La segmentation et le regroupement en locuteurs (SRL) impliquent la détection des locuteurs dans un flux audio et les intervalles pendant lesquels chaque locuteur est actif, c'est-à-dire la détermination de ‘qui parle quand’. La première partie des travaux présentés dans cette thèse exploite une approche de modélisation du locuteur utilisant des clés binaires (BKs) comme solution à la SRL. La modélisation BK est efficace et fonctionne sans données d'entraînement externes, car elle utilise uniquement des données de test. Les contributions présentées incluent l'extraction des BKs basée sur l'analyse spectrale multi-résolution, la détection explicite des changements de locuteurs utilisant les BKs, ainsi que les techniques de fusion SRL qui combinent les avantages des BKs et des solutions basées sur un apprentissage approfondi. La tâche de la SRL est étroitement liée à celle de la reconnaissance ou de la détection du locuteur, qui consiste à comparer deux segments de parole et à déterminer s'ils ont été prononcés par le même locuteur ou non. Même si de nombreuses applications pratiques nécessitent leur combinaison, les deux tâches sont traditionnellement exécutées indépendamment l'une de l'autre. La deuxième partie de cette thèse porte sur une application où les solutions de SRL et de reconnaissance des locuteurs sont réunies. La nouvelle tâche, appelée détection de locuteurs à faible latence, consiste à détecter rapidement les locuteurs connus dans des flux audio à locuteurs multiples. Il s'agit de repenser la SRL en ligne et la manière dont les sous-systèmes de SRL et de détection devraient être combinés au mieux. / Speaker diarization (SD) involves the detection of speakers within an audio stream and the intervals during which each speaker is active, i.e. the determination of ‘who spoken when’. The first part of the work presented in this thesis exploits an approach to speaker modelling involving binary keys (BKs) as a solution to SD. BK modelling is efficient and operates without external training data, as it operates using test data alone. The presented contributions include the extraction of BKs based on multi-resolution spectral analysis, the explicit detection of speaker changes using BKs, as well as SD fusion techniques that combine the benefits of both BK and deep learning based solutions. The SD task is closely linked to that of speaker recognition or detection, which involves the comparison of two speech segments and the determination of whether or not they were uttered by the same speaker. Even if many practical applications require their combination, the two tasks are traditionally tackled independently from each other. The second part of this thesis considers an application where SD and speaker recognition solutions are brought together. The new task, coined low latency speaker spotting (LLSS), involves the rapid detection of known speakers within multi-speaker audio streams. It involves the re-thinking of online diarization and the manner by which diarization and detection sub-systems should best be combined.

The goal of the thesis is the finger-print authentication support within the Linux operating system and the K Desktop Environment (KDE). Theoretical part of the thesis firstly explains main IT security terms and ways to proof the identity. Secondly it describes biometric systems and types of processed biometric characteristics. Lastly the features of finger-prints, their markants as well as types of scanners used in scanning the finger-prints and ways to analyze the scanned material have been elaborated. Practical solution part of the thesis develops and establishes finger-print management application and plugin for KDM graphics login manager.

Heutzutage beeinflusst die Biometrie mehr und mehr unsere Leben. Diese Technologie soll uns Sicherheit und auch Bequemlichkeit erschaffen. Biometrische Systeme ersetzen jeden Tag ältere Sicherheitssysteme und die Firmen versprechen sich mehr Leistung zu bekommen. Aber trotzdem kann man über viele Bereiche der Biometrie sagen, dass sie noch zurückgeblieben sind. In meiner Arbeit analysiere ich den Zustand der ganzen biometrischen Industrie, ich lerne die neuesten Technologien kennen. Die Mängel dieser Industrie sind noch deutlich und es müssen noch viele Innovationen durchgesetzt werden. Ich widme mich meistens der Sicherheit der biometrischen Systeme, konkret orientiere ich mich auf die Fingerabdruckstechnologie. Nach der Analyse der neuesten Angriffe und Sicherheitsvorgänge, werte ich die Technik der Erkennung der feinen Hautbewegungen der Fingerspitzen aus.

[pt] O reconhecimento de humanos pela íris é um dos sistemas mais seguros de identificação biométrica e, motivou a construção de um protótipo de identificação humana baseada na estrutura da íris. O sistema construído é composto de um dispositivo de captura de imagens da íris humana e algoritmos para pré- processamento da imagem, para a representação e o reconhecimento. Cada um dos elementos que compõem o protótipo são avaliados a partir de dois bancos de dados de imagens de íris. Os resultados demonstraram que o dispositivo proposto e os modelos apresentados são capazes de realizar o reconhecimento humano através da íris de forma eficiente. / [en] The recognition of human beings for the Iris is one of the safest systems of biometric identification. This motivated the construction of a prototype for identification of human beings based on the structure of the Iris. The constructed system is composed of a device capable to capture images of the Iris and algorithms for image pre - processing, for the representation and recognition each element composing the prototype is evaluated using two data bases of Iris images. The results have demonstrated that the prototype and the presented models are capable to efficiently identify the human based on Iris structure.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

With the increased use of Android smartphones, the Android Pattern Lock graphical password has become commonplace. The Android Pattern Lock is advantageous in that it is easier to remember and is more complex than a five digit numeric code. However, it is susceptible to a number of attacks, both direct and indirect. This fact shows that the Android Pattern Lock by itself is not enough to protect personal devices. Other means of protection are needed as well. In this thesis I have investigated five methods for the analysis of biometric data as an unnoticable second verification step of the Android Pattern Lock. The methods investigated are the euclidean barycentric anomaly detector, the dynamic time warping barycentric anomaly detector, a one-class support vector machine, the local outlier factor anomaly detector and a normal distribution based anomaly detector. The models were trained using an online training strategy to enable adaptation to changes in the user input behaviour. The model hyperparameters were fitted using a data set with 85 users. The models are then tested with other data sets to illustrate how different phone models and patterns affect the results.        The euclidean barycentric anomaly detector and dynamic time warping (DTW) barycentric anomaly detector have a sub 10 \% equal error rate in both mean and median, while the other three methods have an equal error rate between 15 \% and 20 \% in mean and median. The higher performance of the euclidean and DTW barycentric anomaly detector is likely because they account for the time series nature of the data, while the other methods do not. Each user in the data set have provided each pattern at most 50 times, meaning that the long-term effects of user adaptation could not be studied.

User authentication is vital in a secure system. Authentication is achieved through something a genuine user knows, has, or is. The latter is called biometrics, commonly attributed with fingerprint and face modalities. It is also possible to identify a user based on their behavior, called behavioral biometrics. In this study, keyboard and mouse behavior were considered. Previous research indicate promise for this authentication method. The research however is scarce, old and often not comprehensive. This study focus on two available data sets, the CMU keystroke dynamics dataset and the ReMouse data set. The data was used together with a comprehensive set of multi-class supervised classification machine learning algorithms from the scikit-learn library for Python. By performing hyperparameter optimization, two optimal algorithms with modified hyperparameters were found that improved results compared with previous research. For keystroke dynamics a classifier based on a neural network, multi-layer perceptron, achieved an Equal Error Rate (EER) of 1.26%. For mouse dynamics, a decision tree classifier achieved an EER of 0.43%. The findings indicate that the produced biometric classifiers can be used in an authentication model and importantly to strengthen existing authentication models such as password based login as a safe alternative to traditional Multi-Factor Authentication (MFA). / Användarautentisering är vitalt i ett säkert system. Autentisering genomförs med hjälp av något en genuin användare vet, har eller är. Det senare kallas biometri, ofta ihopkopplat med fingeravtryck och ansiktigenkänning. Det är även möjligt att identifiera en användare baserat på deras beteende, så kallad beteendemässig biometri. I denna studie används tangentbords- och musanvändning. Tidigare forskning tyder på att denna autentiseringsmetod är lovande. Forskningen är dock knapp, äldre och svårbegriplig. Denna studie använder två publika dataset, CMU keystroke dynamics dataset och ReMouse data set. Datan används tillsammans med en utförlig mängd maskininlärningsalgoritmer från scitkit-learn biblioteket för programmeringsspråket Python. Genom att optimera algoritmernas hyper parametrar kunde två stycken optimala klassificerare tas fram som åstadkom förbättrade resultat mot tidigare forskning. För tangentbordsbeteende producerades en klassificerare baserat på neurala nätverk, så kallad multi-layer perceptron som åstadkom en EER på 1.26%. För musrörelser kunde en modell baserat på beslutsträd åstadkomma en EER på 0.43%. Resultatet av dessa upptäckter är att liknande klassificerare kan användas i en autentiseringsmodell men också för att förbättra säkerheten hos etablerade inloggningssätt som exempelvis lösenord och därmed utgöra ett säkert alternativ till traditionell MFA.

Biometrics are widely used in identification systems, such as face, fingerprint, iris, etc. Polar code is the only code that can be strictly proved to achieve channel capacity, and it has been proved to be optimal for channel and source coding. In this degree project, our goal is to apply polar codes algorithms to biometric identification systems, and to design a biometric identification system with high identification accuracy, low system complexity, and good privacy preservation. This degree project has carried out specific and in-depth research in four aspects, following results are achieved: First, idea of polar codes is learnt, for example channel combination, channel splitting, successive cancellation decoding. The successive cancellation and successive cancellation list algorithm are also applied to encoding, which further realizes polar codes for source coding. Second, using autoencoder to process biometrics. Autoencoder is introduced to compress fingerprints into binary sequences of length 1024, it has 5 encoding layers and 12 decoding layers, achieved reconstruction error is 0.03. The distribution is close to Gaussian distribution, and compressed codes are quantized into binary sequences. Properties of sequences are similar with random sequences in terms of entropy, correlation, variance. Third, the identification system under Wyner-Ziv problem is studied with fingerprints. In enrollment phase, encoding algorithms are designed to compress biometrics, and in identification phase, decoding algorithms are designed to estimate the original sequence based on decoded results and noisy sequence. Maximum mutual information method is used to identify users. Results show that with smaller number of users, longer code length, smaller noise, then recognition error rate is lower. Fourth, human faces are used in the generated secret key system. After fully considering the trade off to achieve optimal results, in enrollment phase both public data and secure data are generated, in identification phase user’s index and secret key are estimated. A hierarchical structure is further studied. First, CNN is used to classify the age of faces, and then the generated secret key system is used for identification after narrowing the range. The system complexity is reduced by 80% and the identification accuracy is not reduced. / Biometriska kännetecken används i stor utsträckning i identifieringssystem, kännetecken såsom ansikte, fingeravtryck, iris, etc. Polär kod är den enda koden som strikt bevisats uppnå kanalkapacitet och den har visat sig vara optimal för kanal- och källkodning. Målet med detta examensarbete är att tillämpa polära kodalgoritmer på biometriska identifieringssystem, och att designa ett biometriskt identifieringssystem med hög identifieringsnoggrannhet, låg systemkomplexitet och bra integritetsskydd. Under examensarbetet har det genomförts specifik och djupgående forskning i fyra aspekter, följande resultat har uppnåtts: För det första introduceras idén om polära koder, till exempel kanalkombination, kanaluppdelning, successiv annulleringsavkodning. Algoritmerna för successiv annullering och successiv annulleringslista tillämpas även på kodning,vilket ytterligare realiserar polära koders användning för källkodning. För det andra används autoencoder för att bearbeta biometriska uppgifter. Autoencoder introduceras för att komprimera fingeravtryck till binära sekvenser med längden 1024, den har 5 kodningslager och 12 avkodningslager, det uppnådda rekonstruktionsfelet är 0,03. Fördelningen liknar en normaldistribution och komprimerade koder kvantiseras till binära sekvenser. Egenskaperna för sekvenserna liknar slumpmässiga sekvenser vad gäller entropi, korrelation, varians. För det tredje studeras identifieringssystemet under Wyner-Ziv-problemet med fingeravtryck. I inskrivningsfasen är kodningsalgoritmer utformade för att komprimera biometriska kännetecken, och i identifieringsfasen är avkodningsalgoritmer utformade för att estimera den ursprungliga sekvensen baserat på avkodade resultat och brusiga sekvenser. Maximal ömsesidig informationsmetod används för att identifiera användare. Resultaten visar att med ett mindre antal användare, längre kodlängd och mindre brus så är identifieringsfelfrekvensen lägre. För det fjärde används mänskliga ansikten i det genererade hemliga nyckelsystemet. Efter att noggrant ha övervägt kompromisser fullt ut för att uppnå det optimala resultatet genereras både offentlig data och säker data under registreringsfasen, i identifieringsfasen uppskattas användarens index och säkerhetsnyckel. En hierarkisk struktur studeras vidare. Först används CNN för att klassificera ålder baserat på ansikten och sedan används det genererade hemliga nyckelsystemet för identifiering efter att intervallet har begränsats. Systemkomplexiteten reduceras med 80% men identifieringsnoggrannheten reduceras inte.

Lösenord har länge varit den metod som föredragits av användare för att skydda användarkonton och känslig information. I strävan till att finna enklare, snabbare och säkrare autentiseringsmetoder har biometriska säkerhetslösningar snabbt vuxit i popularitet. Mobiltelefoner har traditionellt skyddats med hjälp av lösenord men har på senare tid även börjat inkludera någon form av biometrisk sensor för autentisering.   Genom att utföra en kvalitativ intervjustudie med IT-forensiker som arbetar på Polismyndigheten inom olika distrikt i Västra Götalands län undersökte denna studie forskningsfrågan: hur har biometriska säkerhetslösningar i jämförelse med lösenord påverkat IT-forensikerns arbete på Polismyndigheten? Studien visar att biometrisk utrustning inte ger extra säkerhet i jämförelse med lösenord, eftersom en bakomliggande säkerhetskod alltid finns till hands ifall den biometriska sensorn slutar fungera. Därmed dras biometriska enheter med samma sorts svagheter som alltid funnits med lösenord. Nyckelord: biometri, lösenord, säkerhet, etik, juridik, IT-forensik. / Passwords have long been the users’ preferred method of choice to protect user accounts and sensitive data. In a strive to find simpler, quicker and more secure forms of authentication methods, biometric security solutions have seen an increased in popularity. Most mobile phones now include a type of biometrical sensors as an option for authentication. By conducting a qualitative interview study with IT-forensics employed by the police force in different districts in Västra Götaland county, this study aims to investigate the research question: How have biometric security solutions in comparison to passwords influenced the working methods of IT-forensics at the Swedish Police Authority? The study shows that biometric security solutions give no added benefit to security in comparison to passwords, because there is always an underlying security code that is ready to be used in case the biometric authentication fails to work. Therefore, biometric devices suffer from the same kinds of weaknesses that have always plagued passwords. Keywords: biometrics, passwords, security, ethics, law, IT-forensics.

<p>Mотив за истраживање је унапређење процеса аутоматског препознавања говорника без обзира на садржај изговоренoг текста.<br />Циљ ове докторске дисертације је дефинисање новог биометријског обележја за препознавање говорника независно од изговореног текста &minus; дводимензионалне информационе ентропије говорног сигнала.<br />Дефинисање новог обележја се врши искључиво у временском домену, па је рачунарска сложеност алгоритма за његово издвајање знатно мања у односу на обележја која се издвајају у фреквенцијском домену. Оцена перформанси дводимензионалне информационе ентропије је урађена над репрезентативним скупом случајно одабраних говорника. Показано је да предложено обележје има малу варијабилност унутар говорног сигнала једног говорника, а велику варијабилност између говорних сигнала различитих говорника.</p> / <p>Motiv za istraživanje je unapređenje procesa automatskog prepoznavanja govornika bez obzira na sadržaj izgovorenog teksta.<br />Cilj ove doktorske disertacije je definisanje novog biometrijskog obeležja za prepoznavanje govornika nezavisno od izgovorenog teksta &minus; dvodimenzionalne informacione entropije govornog signala.<br />Definisanje novog obeležja se vrši isključivo u vremenskom domenu, pa je računarska složenost algoritma za njegovo izdvajanje znatno manja u odnosu na obeležja koja se izdvajaju u frekvencijskom domenu. Ocena performansi dvodimenzionalne informacione entropije je urađena nad reprezentativnim skupom slučajno odabranih govornika. Pokazano je da predloženo obeležje ima malu varijabilnost unutar govornog signala jednog govornika, a veliku varijabilnost između govornih signala različitih govornika.</p> / <p>Тhe motivation for the research is the improvement of the automatic speaker recognition process regardless of the content of spoken text.<br />The objective of this dissertation is to define a new biometric text-independent speaker recognition feature &minus; the two-dimensional informational entropy of speech signal.<br />Definition of the new feature is performed in time domain exclusively, so the computing complexity of the algorithm for feature extraction is significantly lower in comparison to feature extraction in spectral domain. Performance analysis of two-dimensional information entropy is performed on the representative set of randomly chosen speakers. It has been shown that new feature has small within-speaker variability and significant between-speaker variability.</p>