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

Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block: Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%.Comment: a preprint paper accepted in wacv2023 worksho

A Survey of Machine Learning Techniques for Behavioral-Based Biometric User Authentication

Authentication is a way to enable an individual to be uniquely identified usually based on passwords and personal identification number (PIN). The main problems of such authentication techniques are the unwillingness of the users to remember long and challenging combinations of numbers, letters, and symbols that can be lost, forged, stolen, or forgotten. In this paper, we investigate the current advances in the use of behavioral-based biometrics for user authentication. The application of behavioral-based biometric authentication basically contains three major modules, namely, data capture, feature extraction, and classifier. This application is focusing on extracting the behavioral features related to the user and using these features for authentication measure. The objective is to determine the classifier techniques that mostly are used for data analysis during authentication process. From the comparison, we anticipate to discover the gap for improving the performance of behavioral-based biometric authentication. Additionally, we highlight the set of classifier techniques that are best performing for behavioral-based biometric authentication

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

MyDroid : ensinar o meu robô com o Android

Mestrado em Engenharia de Computadores e TelemáticaOs robôs começaram a ser usados em diversas áreas. Os principais objectivos são a optimização de trabalho e redução de custos. No entanto o modo de interacção com estes equipamentos parece não acompanhar esta evolução. Com o surgimento dos smartphones chega uma nova oportunidade. Estes pequenos dispositivos têm uma elevada capacidade de processamento e estão equipamentos com um conjunto de recursos que podem ser usados na interacção homem-máquina. Esta dissertação propõe um conjunto de soluções para interação com robôs, onde o smartphone é o meio usado para interagir (ao nivel de controlo e ensino). Pretende-se assim avaliar se o smartphone é uma alternativa viável para a interação.Robots started to be used in several areas. The main objectives are work optimization and cost reduction. However, an interaction method with these devices does not seem to follow this evolution. With the rise of smartphones comes a new opportunity. These small devices have a high processing capacity and are equipped with a set of resources that can be used for manmachine interaction. This thesis proposes a set of solutions for interaction with robots, where a smartphone is the instrument used to interact (both commanding and teaching). It is intended to assess whether the smartphone is a viable alternative for interaction

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

Internet of Things (IoT) Applications With Diverse Direct Communication Methods

Title from PDF of title page viewed August 28, 2017Dissertation advisor: Baek-Young ChoiVitaIncludes bibliographical references (pages 124-138)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2016Internet of Things (IoT) is a network of physical objects or things that are embedded with electronics, software, sensors, and network connectivity - which enable the object to collect and exchange data. Rapid proliferation of IoT is driving the intelligence in things used daily in homes, workplaces and industry. The IoT devices typically communicate via radio frequency (RF), such as WiFi and Bluetooth. In this dissertation we deeply analyze the various characteristics of different wireless communication methods in terms of range, energy-efficiency, and radiation pattern. We find that a well-established communication method might not be the most efficient, and other alternate communication methods with the desired properties for a particular application could exist. We exploit radically alternative, innovative, and complimentary wireless communication methods, including radio frequency, infrared (IR), and visible lights, through the IoT applications we have designed and built with those. We have developed various IoT applications which provide security and authentication, enable vehicular communications with smartphones or other smart devices, provide energy-efficient and accurate positioning to smart devices, and enable energy-efficient communications in Industrial Internet of Things (IIoT).Introduction -- Optical wireless authentication for SMART devices using an onboard ambient light sensor -- Smartphome based CAR2X-communication with wifi beacon stuffing for vulnerable road user safety -- Energy-efficient cooperative opportunistic positioning heterogeneous Smart devices -- Reducing and balancing energy consumption in Indistrial Internet of Things (IIoT) -- Optical wireless unlocking for Smart door locks using Smartphones -- Summary and future direction