Integrating passive ubiquitous surfaces into human-computer interaction

Mobile technologies enable people to interact with computers ubiquitously. This dissertation investigates how ordinary, ubiquitous surfaces can be integrated into human-computer interaction to extend the interaction space beyond the edge of the display. It turns out that acoustic and tactile features generated during an interaction can be combined to identify input events, the user, and the surface. In addition, it is shown that a heterogeneous distribution of different surfaces is particularly suitable for realizing versatile interaction modalities. However, privacy concerns must be considered when selecting sensors, and context can be crucial in determining whether and what interaction to perform.Mobile Technologien ermöglichen den Menschen eine allgegenwärtige Interaktion mit Computern. Diese Dissertation untersucht, wie gewöhnliche, allgegenwärtige Oberflächen in die Mensch-Computer-Interaktion integriert werden können, um den Interaktionsraum über den Rand des Displays hinaus zu erweitern. Es stellt sich heraus, dass akustische und taktile Merkmale, die während einer Interaktion erzeugt werden, kombiniert werden können, um Eingabeereignisse, den Benutzer und die Oberfläche zu identifizieren. Darüber hinaus wird gezeigt, dass eine heterogene Verteilung verschiedener Oberflächen besonders geeignet ist, um vielfältige Interaktionsmodalitäten zu realisieren. Bei der Auswahl der Sensoren müssen jedoch Datenschutzaspekte berücksichtigt werden, und der Kontext kann entscheidend dafür sein, ob und welche Interaktion durchgeführt werden soll

A. Eye Detection Using Varients of Hough Transform B. Off-Line Signature Verification

PART (A): EYE DETECTION USING VARIANTS OF HOUGH TRANSFORM: Broadly eye detection is the process of tracking the location of human eye in a face image. Previous approaches use complex techniques like neural network, Radial Basis Function networks, Multi-Layer Perceptrons etc. In the developed project human eye is modeled as a circle (iris; the black circular region of eye) enclosed inside an ellipse (eye-lashes). Due to the sudden intensity variations in the iris with respect the inner region of eye-lashes the probability of false acceptance is very less. Since the image taken is a face image the probability of false acceptance further reduces. Hough transform is used for circle (iris) and ellipse (eye-lash) detection. Hough transform was the obvious choice because of its resistance towards the holes in the boundary and noise present in the image. Image smoothing is done to reduce the presence of noise in the image further it makes the image better for further processing like edge detection (Prewitt method). Compared to the aforementioned models the proposed model is simple and efficient. The proposed model can further be improved by including various features like orientation angle of eye-lashes (which is assumed constant in the proposed model), and by making the parameters adaptive. PART (B): OFF-LINE SIGNATURE VERIFICATION: Hand-written signature is widely used for authentication and identification of individual. It has been the target for fraudulence ever since. A novel off-line signature verification algorithm has been developed and tested successfully. Since the hand-written signature can be random, because of presence of various curves and features, techniques like character recognition cannot be applied for signature verification. The proposed algorithm incorporates a soft-computing technique “CLUSTERING” for extraction of feature points from the image of the signature. These feature points or centers are updated using the clustering update equations for required number of times, then these acts as extracted feature points of the signature image. To avoid interpersonal variation 6 to 8 signature images of the same person are taken and feature points are trained. These trained feature points are compared with the test signature images and based on a specific threshold, the signature is declared original or forgery. This approach works well if there is a high variation in the original signature, but for signatures with low variation, it produces incorrect results

Offline Signature Verification for Arabic Language

Biometrics relies on biological features (e.g. finger print, iris or the retina) or behavioral features (voice, signature). Those features can be used for identity verification for an individual. For this it became one of the most trusted and natural ways to identify a person and controlling access to the systems. Signature is a behavioral biometric. Signature is not unique like iris or finger print as it can be forged. Automatic signature verification is divided into two areas depending on the way of data capturing: offline and online signature verification. In offline signature verification, the signature is scanned from a document using a scanner to get the image of the signature. In online signature, a digitizing tablet is used to collect the movements during the signing. In this work we present a system for offline signature verification. In this system the user has to submit a number of signatures which are used to extract two types of features, statistical features and structural features. A vector obtained from each of them is used to train propagation neural net in the verification stage. A test signature is then taken from the user, to compare it with those the net had been trained with. A test experiment was carried out with two sets of data are collected. One set is used as a training set for the propagation neural net in its verification stage. This set with four signatures form each user is used for the training purpose. The second set consisting of one sample of signature for each of the 20 persons is used as a test set for the system. A negative identification test was carried out using a signature of one person to test others’ signatures. The system gave encouraging results

Sign Here!

Sign Here! Handwriting in the Age of New Media features a number of articles from different fields, reaching from cultural and media studies to literature, film and art, and from philosophy and information studies to law and archival studies. Questions addressed in this book are: Will handwriting disappear in the age of new (digital) media? What happens to important cultural and legal concepts, such as original, copy, authenticity, reproducibility, uniqueness, and iterability? Where is the writing hand to be located if handwriting is performed not immediately 'by hand' but when it is (re)mediated by electronic or artistic media? Sign Here! Handwriting in the Age of New Media is the first part in the series Transformations in Art and Culture

Contributions to non-conventional biometric systems : improvements on the fingerprint, facial and handwriting recognition approach

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Os sistemas biométricos são amplamente utilizados pela sociedade. A maioria das aplicações desses sistemas está associada à identificação civil e à investigação criminal. No entanto, com o tempo, o desempenho dos métodos tradicionais de biometria está chegando ao limite. Neste contexto, sistemas biométricos emergentes ou não convencionais estão ganhando importância. Embora promissores, novos sistemas, assim como qualquer nova tecnologia, trazem consigo não apenas potencialidades, mas também fragilidades. Este trabalho apresenta contribuições para três importantes sistemas biométricos não convencionais (SBNC): impressão digital, reconhecimento facial e reconhecimento de escrita. No que diz respeito às impressões digitais, este trabalho apresenta um novo método para detectar a vida em dispositivos de impressão digital multivista sem toque, utilizando descritores de textura e redes neurais artificiais. Com relação ao reconhecimento facial, um método de reconhecimento de faces baseado em algoritmos de característica invariante à escala (SIFT e SURF) que opera sem a necessidade de treinamento prévio do classificador e que realiza o rastreamento de indivíduos em ambientes não controlados é apresentado. Finalmente, um método de baixo custo que usa sinais de acelerômetro e giroscópio obtidos a partir de um sensor acoplado a canetas convencionais para realizar o reconhecimento em tempo real de assinaturas é apresentado. Resultados mostram que os métodos propostos são promissores e que juntos podem contribuir para o aprimoramento dos SBNCCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).Biometric systems are widely used by society. Most applications are associated with civil identification and criminal investigation. However, over time, traditional methods of performing biometrics have been reaching their limits. In this context, emerging or nonconventional biometric systems (NCBS) are gaining ground. Although promising, new systems, as well as any new technology, bring not only potentialities but also weaknesses. This work presents contributions to three important non-conventional biometric systems: fingerprint, facial, and handwriting recognition. With regard to fingerprints, this work presents a novel method for detecting life on Touchless Multi-view Fingerprint Devices, using Texture Descriptors and Artificial Neural Networks. With regard to face recognition, a facial recognition method is presented, based on Scale Invariant Feature Algorithms (SIFT and SURF), that operates without the need of previous training of a classifier and can be used to track individuals in an unconstrained environment. Finally, a low-cost on-line handwriting signature recognition method that uses accelerometer and gyroscope signals obtained from a sensor coupled to conventional pens to identify individuals in real time is presented. Results show that the proposed methods are promising and that together may contribute to the improvement of the NCB

Detecting Forgery: Forensic Investigation of Documents

Detecting Forgery reveals the complete arsenal of forensic techniques used to detect forged handwriting and alterations in documents and to identify the authorship of disputed writings. Joe Nickell looks at famous cases such as Clifford Irving\u27s autobiography of Howard Hughes and the Mormon papers of document dealer Mark Hoffman, as well as cases involving works of art. Detecting Forgery is a fascinating introduction to the growing field of forensic document examination and forgery detection. Seldom does a book about forgery come along containing depth of subject matter in addition to presenting clear and understandable information. This book has both, plus a readability that is accessible to those studying questioned documents as well as seasoned experts. -- Journal of Forensic Identification The author\u27s expertise in historical documents is unmistakably evident throughout the book. Once I began reading, I found it hard to put down. -- Journal of Questioned Document Examination Guides the reader through various methods and techniques of identifying fakes and phone manuscripts. -- Manchester (KY) Enterprisehttps://uknowledge.uky.edu/upk_legal_studies/1000/thumbnail.jp

Leveraging Law Office Technology

Meeting proceedings of a seminar by the same name, held April 7, 2021

The Development of a graduate course on identity management for the Department of Networking, Security, and Systems Administration

Digital identities are being utilized more than ever as a means to authenticate computer users in order to control access to systems, web services, and networks. To maintain these digital identities, administrators turn to Identity Management solutions to offer protection for users, business partners, and networks. This paper proposes an analysis of Identity Management to be accomplished in the form of a graduate level course of study for a ten-week period for the Networking, Security, and Systems Administration department at Rochester Institute of Technology. This course will be designed for this department because of its emphasis on securing, protecting, and managing the identities of users within and across networks. Much of the security-related courses offered by the department focus primarily on security within enterprises. Therefore, Identity Management, a topic that is becoming more popular within enterprises each day, would compliment these courses. Students that enroll in this course will be more equipped to satisfy the needs of modern enterprises when they graduate because they will have a better understanding of how to address security issues that involve managing user identities across networks, systems, and enterprises. This course will focus on several aspects of Identity Management and its use in enterprises today. Covered during the course will be the frameworks of Identity Management, for instance, Liberty Identity Federation Framework and OASIS SAML 2.0; the Identity Management models; and some of the major Identity Management solutions that are in use today such as Liberty Alliance, Microsoft Passport, and Shibboleth. This course will also provide the opportunity to gain hands on experience by facilitating exemplar technologies used in laboratory investigations

Discriminative preprocessing of speech : towards improving biometric authentication

Im Rahmen des "SecurePhone-Projektes" wurde ein multimodales System zur Benutzerauthentifizierung entwickelt, das auf ein PDA implementiert wurde. Bei der vollzogenen Erweiterung dieses Systems wurde der Möglichkeit nachgegangen, die Benutzerauthentifizierung durch eine auf biometrischen Parametern (E.: "feature enhancement") basierende Unterscheidung zwischen Sprechern sowie durch eine Kombination mehrerer Parameter zu verbessern. In der vorliegenden Dissertation wird ein allgemeines Bezugssystem zur Verbesserung der Parameter präsentiert, das ein mehrschichtiges neuronales Netz (E.: "MLP: multilayer perceptron") benutzt, um zu einer optimalen Sprecherdiskrimination zu gelangen. In einem ersten Schritt wird beim Trainieren des MLPs eine Teilmenge der Sprecher (Sprecherbasis) berücksichtigt, um die zugrundeliegenden Charakteristika des vorhandenen akustischen Parameterraums darzustellen. Am Ende eines zweiten Schrittes steht die Erkenntnis, dass die Größe der verwendeten Sprecherbasis die Leistungsfähigkeit eines Sprechererkennungssystems entscheidend beeinflussen kann. Ein dritter Schritt führt zur Feststellung, dass sich die Selektion der Sprecherbasis ebenfalls auf die Leistungsfähigkeit des Systems auswirken kann. Aufgrund dieser Beobachtung wird eine automatische Selektionsmethode für die Sprecher auf der Basis des maximalen Durchschnittswertes der Zwischenklassenvariation (between-class variance) vorgeschlagen. Unter Rückgriff auf verschiedene sprachliche Produktionssituationen (Sprachproduktion mit und ohne Hintergrundgeräusche; Sprachproduktion beim Telefonieren) wird gezeigt, dass diese Methode die Leistungsfähigkeit des Erkennungssystems verbessern kann. Auf der Grundlage dieser Ergebnisse wird erwartet, dass sich die hier für die Sprechererkennung verwendete Methode auch für andere biometrische Modalitäten als sinnvoll erweist. Zusätzlich wird in der vorliegenden Dissertation eine alternative Parameterrepräsentation vorgeschlagen, die aus der sog. "Sprecher-Stimme-Signatur" (E.: "SVS: speaker voice signature") abgeleitet wird. Die SVS besteht aus Trajektorien in einem Kohonennetz (E.: "SOM: self-organising map"), das den akustischen Raum repräsentiert. Als weiteres Ergebnis der Arbeit erweist sich diese Parameterrepräsentation als Ergänzung zu dem zugrundeliegenden Parameterset. Deshalb liegt eine Kombination beider Parametersets im Sinne einer Verbesserung der Leistungsfähigkeit des Erkennungssystems nahe. Am Ende der Arbeit sind schließlich einige potentielle Erweiterungsmöglichkeiten zu den vorgestellten Methoden zu finden. Schlüsselwörter: Feature Enhancement, MLP, SOM, Sprecher-Basis-Selektion, SprechererkennungIn the context of the SecurePhone project, a multimodal user authentication system was developed for implementation on a PDA. Extending this system, we investigate biometric feature enhancement and multi-feature fusion with the aim of improving user authentication accuracy. In this dissertation, a general framework for feature enhancement is proposed which uses a multilayer perceptron (MLP) to achieve optimal speaker discrimination. First, to train this MLP a subset of speakers (speaker basis) is used to represent the underlying characteristics of the given acoustic feature space. Second, the size of the speaker basis is found to be among the crucial factors affecting the performance of a speaker recognition system. Third, it is found that the selection of the speaker basis can also influence system performance. Based on this observation, an automatic speaker selection approach is proposed on the basis of the maximal average between-class variance. Tests in a variety of conditions, including clean and noisy as well as telephone speech, show that this approach can improve the performance of speaker recognition systems. This approach, which is applied here to feature enhancement for speaker recognition, can be expected to also be effective with other biometric modalities besides speech. Further, an alternative feature representation is proposed in this dissertation, which is derived from what we call speaker voice signatures (SVS). These are trajectories in a Kohonen self organising map (SOM) which has been trained to represent the acoustic space. This feature representation is found to be somewhat complementary to the baseline feature set, suggesting that they can be fused to achieve improved performance in speaker recognition. Finally, this dissertation finishes with a number of potential extensions of the proposed approaches. Keywords: feature enhancement, MLP, SOM, speaker basis selection, speaker recognition, biometric, authentication, verificatio

Non-Intrusive Subscriber Authentication for Next Generation Mobile Communication Systems

Merged with duplicate record 10026.1/753 on 14.03.2017 by CS (TIS)The last decade has witnessed massive growth in both the technological development, and the consumer adoption of mobile devices such as mobile handsets and PDAs. The recent introduction of wideband mobile networks has enabled the deployment of new services with access to traditionally well protected personal data, such as banking details or medical records. Secure user access to this data has however remained a function of the mobile device's authentication system, which is only protected from masquerade abuse by the traditional PIN, originally designed to protect against telephony abuse. This thesis presents novel research in relation to advanced subscriber authentication for mobile devices. The research began by assessing the threat of masquerade attacks on such devices by way of a survey of end users. This revealed that the current methods of mobile authentication remain extensively unused, leaving terminals highly vulnerable to masquerade attack. Further investigation revealed that, in the context of the more advanced wideband enabled services, users are receptive to many advanced authentication techniques and principles, including the discipline of biometrics which naturally lends itself to the area of advanced subscriber based authentication. To address the requirement for a more personal authentication capable of being applied in a continuous context, a novel non-intrusive biometric authentication technique was conceived, drawn from the discrete disciplines of biometrics and Auditory Evoked Responses. The technique forms a hybrid multi-modal biometric where variations in the behavioural stimulus of the human voice (due to the propagation effects of acoustic waves within the human head), are used to verify the identity o f a user. The resulting approach is known as the Head Authentication Technique (HAT). Evaluation of the HAT authentication process is realised in two stages. Firstly, the generic authentication procedures of registration and verification are automated within a prototype implementation. Secondly, a HAT demonstrator is used to evaluate the authentication process through a series of experimental trials involving a representative user community. The results from the trials confirm that multiple HAT samples from the same user exhibit a high degree of correlation, yet samples between users exhibit a high degree of discrepancy. Statistical analysis of the prototypes performance realised early system error rates of; FNMR = 6% and FMR = 0.025%. The results clearly demonstrate the authentication capabilities of this novel biometric approach and the contribution this new work can make to the protection of subscriber data in next generation mobile networks.Orange Personal Communication Services Lt