Advanced Biometric Technologies: Emerging Scenarios and Research Trends

Biometric systems are the ensemble of devices, procedures, and algorithms for the automatic recognition of individuals by means of their physiological or behavioral characteristics. Although biometric systems are traditionally used in high-security applications, recent advancements are enabling the application of these systems in less-constrained conditions with non-ideal samples and with real-time performance. Consequently, biometric technologies are being increasingly used in a wide variety of emerging application scenarios, including public infrastructures, e-government, humanitarian services, and user-centric applications. This chapter introduces recent biometric technologies, reviews emerging scenarios for biometric recognition, and discusses research trends

Adapting to Movement Patterns for Face Recognition on Mobile Devices

Facial recognition is becoming an increasingly popular way to authenticate users, helped by the increased use of biometric technology within mobile devices, such as smartphones and tablets. Biometric systems use thresholds to identify whether a user is genuine or an impostor. Traditional biometric systems are static (such as eGates at airports), which allow the operators and developers to create an environment most suited for the successful operation of the biometric technology by using a fixed threshold value to determine the authenticity of the user. However, with a mobile device and scenario, the operational conditions are beyond the control of the developers and operators. In this paper, we propose a novel approach to mobile biometric authentication within a mobile scenario, by offering an adaptive threshold to authenticate users based on the environment, situations and conditions in which they are operating the device. Utilising smartphone sensors, we demonstrate the creation of a successful scenario classification. Using this, we propose our idea of an extendable framework to allow multiple scenario thresholds. Furthermore, we test the concept with data collected from a smartphone device. Results show that using an adaptive scenario threshold approach can improve the biometric performance, and hence could allow manufacturers to produce algorithms that perform consistently in multiple scenarios without compromising security, allowing an increase in public trust towards the use of the technology

Enhancing Proprioception and Regulating Cognitive Load in Neurodiverse Populations through Biometric Monitoring with Wearable Technologies

This paper considers the realm of wearable technologies and their prospective applications for individuals with neurodivergent conditions, specifically Autism Spectrum Disorders (ASDs). The study undertakes a multifaceted analysis that encompasses biomarker sensing technologies, AI-driven biofeedback mechanisms, and haptic devices, focusing on their implications for enhancing proprioception and social interaction among neurodivergent populations. While wearables offer a range of opportunities for societal advancement, a discernable gap remains: a scarcity of consumer-oriented applications tailored to the unique physiological and psychological needs of these individuals. Key takeaways underscore the emergent promise of tailored auditory stimuli in workplace dynamics and the efficacy of haptic feedback in sensory substitution. The investigation concludes with an urgent call for multidisciplinary research aimed at the development of specific consumer applications, rigorous empirical validation, and an ethical framework encompassing data privacy and user consent. As the pervasiveness of technology in daily life continues to expand, the article posits that there is an imperative for future research to shift from generalized solutions to individualized applications, thereby ensuring that the spectrum of wearable technology truly accommodates the full scope of human neurodiversity

Bioelectrical User Authentication

There has been tremendous growth of mobile devices, which includes mobile phones, tablets etc. in recent years. The use of mobile phone is more prevalent due to their increasing functionality and capacity. Most of the mobile phones available now are smart phones and better processing capability hence their deployment for processing large volume of information. The information contained in these smart phones need to be protected against unauthorised persons from getting hold of personal data. To verify a legitimate user before accessing the phone information, the user authentication mechanism should be robust enough to meet present security challenge. The present approach for user authentication is cumbersome and fails to consider the human factor. The point of entry mechanism is intrusive which forces users to authenticate always irrespectively of the time interval. The use of biometric is identified as a more reliable method for implementing a transparent and non-intrusive user authentication. Transparent authentication using biometrics provides the opportunity for more convenient and secure authentication over secret-knowledge or token-based approaches. The ability to apply biometrics in a transparent manner improves the authentication security by providing a reliable way for smart phone user authentication. As such, research is required to investigate new modalities that would easily operate within the constraints of a continuous and transparent authentication system. This thesis explores the use of bioelectrical signals and contextual information for non-intrusive approach for authenticating a user of a mobile device. From fusion of bioelectrical signals and context awareness information, three algorithms where created to discriminate subjects with overall Equal Error Rate (EER of 3.4%, 2.04% and 0.27% respectively. Based vii | P a g e on the analysis from the multi-algorithm implementation, a novel architecture is proposed using a multi-algorithm biometric authentication system for authentication a user of a smart phone. The framework is designed to be continuous, transparent with the application of advanced intelligence to further improve the authentication result. With the proposed framework, it removes the inconvenience of password/passphrase etc. memorability, carrying of token or capturing a biometric sample in an intrusive manner. The framework is evaluated through simulation with the application of a voting scheme. The simulation of the voting scheme using majority voting improved to the performance of the combine algorithm (security level 2) to FRR of 22% and FAR of 0%, the Active algorithm (security level 2) to FRR of 14.33% and FAR of 0% while the Non-active algorithm (security level 3) to FRR of 10.33% and FAR of 0%

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

Participative Urban Health and Healthy Aging in the Age of AI

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2022, held in Paris, France, in June 2022. The 15 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 33 submissions. They cover topics such as design, development, deployment, and evaluation of AI for health, smart urban environments, assistive technologies, chronic disease management, and coaching and health telematics systems

Towards a framework to promote the development of secure and usable online information security applications

The proliferation of the internet and associated online activities exposes users to numerous information security (InfoSec) threats. Such online activities attract a variety of online users who include novice computer users with no basic InfoSec awareness knowledge. Information systems that collect and use sensitive and confidential personal information of users need to provide reliable protection mechanisms to safeguard this information. Given the constant user involvement in these systems and the notion of users being the weakest link in the InfoSec chain, technical solutions alone are insufficient. The usability of online InfoSec systems can play an integral role in making sure that users use the applications effectively, thereby improving the overall security of the applications. The development of online InfoSec systems calls for addressing the InfoSec problem as a social problem, and such development must seek to find a balance between technical and social aspects. The research addressed the problem of usable security in online InfoSec applications by using an approach that enabled the consideration of both InfoSec and usability in viewing the system as a socio-technical system with technical and social sub-systems. Therefore, the research proposed a socio-technical framework that promotes the development of usable security for online information systems using online banking as a case study. Using a convergent mixed methods research (MMR) design, the research collected data from online banking users through a survey and obtained the views of online banking developers through unstructured interviews. The findings from the two research methods contributed to the selection of 12 usable security design principles proposed in the sociotechnical information security (STInfoSec) framework. The research contributed to online InfoSec systems theory by developing a validated STInfoSec framework that went through an evaluation process by seven field experts. Although intended for online banking, the framework can be applied to other similar online InfoSec applications, with minimum adaptation. The STInfoSec framework provides checklist items that allow for easy application during the development process. The checklist items can also be used to evaluate existing online banking websites to identify possible usable security problems.Computer ScienceD. Phil. (Computer Science

Anxolotl - An Anxiety Companion App

Dissertação para obtenção do Grau de Mestre em Engenharia Informática e de ComputadoresA Organização Mundial de Saúde apresentou as perturbações mentais como os maiores contribuintes para incapacidade global em 2015, com os distúrbios de ansiedade a ocuparem a sexta posição. Distúrbios de ansiedade têm um alta prevalência na sociedade, e apresentam sintomas precoces que podem ser detetados. Nesta tese, produzimos um sistema capaz de detetar sintomas de distúrbios de ansiedade antes que a doença se instale por completo. Adicionalmente, queremos dar outra opção a portadores, monitorizando o seu estado mental e oferecendo a hipótese de tratarem dos seus níveis de ansiedade antes que apareçam mais sintomas. Aqui introduzimos um sistema de saúde móvel, entitulado de Anxolotl, que pode detetar e classificar níveis de ansiedade multiclasse e detetar níveis binários de estados de pânico . A nossa solução é composta por: datacenter, com o objectivo de guardar dados fisiológicos anónimos, e aplicar modelos de aprendizagem automática; broker de mensagens, que irá providenciar escalaabilidade e habilidade de desacoplamento no sistema; aplicação móvel, que funcionará em conjunto com um wearable para capturar dados fisiológicos. A nossa applicação é capaz de detetar e monitorizar diariamente, os níveis de ansiedade e pânico do utilizador, filtrando dados dúbios com base em atividade física. A aplicação também apresenta múltiplos exercícios de respiração guiados, bem como meditações acompanhadas para vários cenários de saúde mental. O nosso modelo de deteção de ansiedade foi capaz de apresentar uma precisão de 92% e um f1-Score de 90% na classificação de ansiedade multiclasse, treinando com um dataset com 124 entradas, enquanto que o nosso modelo de deteção de pânico apresenta uma precisão de 94% e um f1-Score de 94%. Estes valores foram atingindos utilizando maioritariamente dados de ritmo cardíaco. O código dos modelos está disponível em https://github.com/nunogoms/Anxolotl-engines.World Health Organization referred that common mental health disorders were the biggest contributors to global disability during the year of 2015, with anxiety disorders occupying the 6th position. Currently, anxiety disorders have high prevalence in society, and present early symptoms that are suited to be detected. With this thesis, we intend to produce a system capable of detecting the anxiety disorder early symptoms before the onset of the full range of symptoms. Additionally, we want to give another option to people already affected, in the form of monitoring their mental health, and the ability for them to react to their anxiety state quickly. Herein, we are introducing a mobile health system — Anxolotl, that can detect and classify multi class anxiety levels and detect binary panic states. Our solution is composed by: a datacenter, intended to store anonymous physiological data and applying the machine learning models; a message broker, aiming to provide scalability and decoupling to the system; and, finally a mobile app, which will work in tandem with a wearable to capture physiological data. The app is able to track and monitor, on a daily basis, its user’s anxiety and panic levels, filtering when the data is unreliable based on activity. It also presents the users with guided breathing exercises for multiple mental health scenarios as well as some guided meditations, in an effort to help its users. The Anxiety Engine model provided a 92% accuracy and 90% f1-Score in classifying multi-class anxiety levels, training and testing with a dataset containing 124 entries, and our binary Panic Engine had an accuracy of 94% and a f1-Score of 94%. Both these scenarios were mainly achieved by using heart rate data, activity context was also used in some scenarios. The code for these models is available at https://github.com/nunogoms/Anxolotl-engines.N/