An overview of touchless 2D fingerprint recognition

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

A Biometric Approach to Prevent False Use of IDs

What is your username? What is your password? What is your PIN number? These are some of the commonly used key questions users need to answer accurately in order to verify their identity and gain access to systems and their own data. Passwords, Personal Identification Numbers (PINs) and ID cards are different means of tokens used to identify a person, but these can be forgotten, stolen or lost. Currently, University of Hertfordshire (UH) carries out identity checks by checking the photograph on an ID card during exams. Other processes such as attendance monitoring and door access control require tapping the ID card on a reader. These methods can cause issues such as unauthorised use of ID card on attendance system and door access system if ID card is found, lost or borrowed. During exams, this could lead to interruptions when carrying out manual checks. As the invigilator carries out checks whilst the student is writing an exam, it is often difficult to see the student’s face as they face down whilst writing the exam. They cannot be disturbed for the ID check process. Students are also required to sign a manual register as they walk into the exam room. This process is time consuming. A more robust approach to identification of individuals that can avoid the above mentioned limitations of the traditional means, is the use of biometrics. Fingerprint was the first biometric modality that has been used. In comparison to other biometric modalities such as signature and face recognition, fingerprint is highly unique, accepted and leads to a more accurate matching result. Considering these properties of fingerprint biometrics, it has been explored in the research study presented in this thesis to enhance the efficiency and the reliability of the University’s exam process. This thesis focuses on using fingerprint recognition technology in a novel approach to check identity for exams in a University environment. Identifying a user using fingerprints is not the only aim of this project. Convenience and user experience play vital roles in this project whilst improving speed and processes at UH

Autenticación por huella dactilar para dispositivos Android mediante captura de imágenes

En el presente proyecto se ha desarrollado una aplicación para dispositivos móviles con Sistema Operativo Android. Esta aplicación es capaz de realizar la autenticación de personas mediante la captura de las huellas dactilares de las mismas con la cámara del propio dispositivo. El presente proyecto afronta el reto de no disponer de sensores de huella dedicados, que es la forma de capturar la huella a tratar, incluso en los dispositivos móviles de ultima generación (iPhone 5S, Galaxy S5), en los que se incorpora esta funcionalidad. Otro importante reto en el presente proyecto es el de implementar en un dispositivo móvil, como teléfonos o tablets, los algoritmos necesarios para conseguir la autenticación. La aplicación desarrollada tiene una doble vertiente. Por un lado es capaz de realizar la autenticación en tiempo real de un número limitado de usuarios, cuyas huellas se almacenan localmente en la memoria del dispositivo. El número de usuarios está limitado por el tiempo necesario para comparar la huella a identificar contra las demás huellas almacenadas. Por otro lado la aplicación es capaz de generar una comparación cruzada de todas las huellas alojadas en una base de datos en DropBox1, a la cual se “suben” todas las huellas capturadas por el dispositivo. La propia aplicación es capaz de crear una hoja Excel en la cual se refleja el resultado obtenido de la comparación de cada huella con todas las demás. Para lograr este objetivo, se ha optimizado el algoritmo de autenticación para adoptar una solución de compromiso entre fiabilidad y tiempo de proceso

Secure and Usable Behavioural User Authentication for Resource-Constrained Devices

Robust user authentication on small form-factor and resource-constrained smart devices, such as smartphones, wearables and IoT remains an important problem, especially as such devices are increasingly becoming stores of sensitive personal data, such as daily digital payment traces, health/wellness records and contact e-mails. Hence, a secure, usable and practical authentication mechanism to restrict access to unauthorized users is a basic requirement for such devices. Existing user authentication methods based on passwords pose a mental demand on the user's part and are not secure. Behavioural biometric based authentication provides an attractive means, which can replace passwords and provide high security and usability. To this end, we devise and study novel schemes and modalities and investigate how behaviour based user authentication can be practically realized on resource-constrained devices. In the first part of the thesis, we implemented and evaluated the performance of touch based behavioural biometric on wearables and smartphones. Our results show that touch based behavioural authentication can yield very high accuracy and a small inference time without imposing huge resource requirements on the wearable devices. The second part of the thesis focus on designing a novel hybrid scheme named BehavioCog. The hybrid scheme combined touch gestures (behavioural biometric) with challenge-response based cognitive authentication. Touch based behavioural authentication is highly usable but is prone to observation attacks. While cognitive authentication schemes are highly resistant to observation attacks but not highly usable. The hybrid scheme improves the usability of cognitive authentication and improves the security of touch based behavioural biometric at the same time. Next, we introduce and evaluate a novel behavioural biometric modality named BreathPrint based on an acoustics obtained from individual's breathing gestures. Breathing based authentication is highly usable and secure as it only requires a person to breathe and low observability makes it secure against spoofing and replay attacks. Our investigation with BreathPrint showed that it could be used for efficient real-time authentication on multiple standalone smart devices especially using deep learning models