Development Of Fingerprint Biometric Attendance Management System Using Wireless Connectivity

In this paper, we propose an integrated biometric access system for attendance management based on fingerprint identification and authentication for restricted area using wireless connectivity. Maintaining the attendance record in institutions, companies and organisations is an imperative factor, maintaining so manually is herculean task. Along with it, institutions with single machine and more crowd makes this work more complicated to make this easier, an efficient Biometric Fingerprint Attendance Management system is proposed. This system registers the user and accepts biometric input through use of mobile network, and all records will be saved for subsequent operations. Since input image is accepted through mobile, it provides greater portability and reduces need for any specific biometric hardware, which in turn reduces the hardware cost. It further provides and facilities to calculate and generate monthly report of attendance in order to reduce any human errors during calculations. Thus, the proposed system will help to improve the productivity of any organization if properly implemented. DOI: 10.17762/ijritcc2321-8169.150315

Perceptions of Risk and Security Concerns with Mobile Devices using Biometric vs Traditional Authentication Methods

Authentication methods on mobile devices provide an important layer of security. Many types of authentication methods exist, some traditional and some biometric-based. In this study, we use a survey method to examine whether the presence and type of an authentication method affect perceptions of risk and security concerns around three specific types of mobile device actions: banking, health, and activities with personally identifiable information (PII). We also survey users’ general perceptions of trust, usefulness, convenience, and ease of use toward authentication methods, both traditional and biometric. We find that users’ perceptions of risk and security concerns change when users consider the type of authentication method present on a device. While traditional methods are still more familiar to most users, we also find that perceptions of biometric-based methods are more similar to perceptions of traditional methods than in the past

Data Behind Mobile Behavioural Biometrics – a Survey

Behavioural biometrics are becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which can’t be exploited to extract behavioural biometric data. In this paper, we investigate data in behavioural biometrics and how this data is used in experiments, especially examining papers that introduce new datasets. We will not examine performance accomplished by the algorithms used since a system’s performance is enormously affected by the data used, its amount and quality. Altogether, 32 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. We offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. We further look at the General Data Protection Regulation, and its significance to the scientific research in the field of biometrics. It is possible to conclude that there is a need for publicly available datasets with comprehensive experimental protocols, similarly established in facial recognition

BioTouchPass: Handwritten Passwords for Touchscreen Biometrics

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibleThis work enhances traditional authentication systems based on Personal Identification Numbers (PIN) and One- Time Passwords (OTP) through the incorporation of biometric information as a second level of user authentication. In our proposed approach, users draw each digit of the password on the touchscreen of the device instead of typing them as usual. A complete analysis of our proposed biometric system is carried out regarding the discriminative power of each handwritten digit and the robustness when increasing the length of the password and the number of enrolment samples. The new e-BioDigit database, which comprises on-line handwritten digits from 0 to 9, has been acquired using the finger as input on a mobile device. This database is used in the experiments reported in this work and it is available together with benchmark results in GitHub1. Finally, we discuss specific details for the deployment of our proposed approach on current PIN and OTP systems, achieving results with Equal Error Rates (EERs) ca. 4.0% when the attacker knows the password. These results encourage the deployment of our proposed approach in comparison to traditional PIN and OTP systems where the attack would have 100% success rate under the same impostor scenarioThis work has been supported by projects: BIBECA (MINECO), Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017) and by UAM-CecaBank. Ruben Tolosana is supported by a FPU Fellowship from Spanish MEC