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

Attacking a smartphone biometric fingerprint system:a novice’s approach

Biometric systems on mobile devices are an increasingly ubiquitous method for identity verification. The majority of contemporary devices have an embedded fingerprint sensor which may be used for a variety of transactions including unlock a device or sanction a payment. In this study we explore how easy it is to successfully attack a fingerprint system using a fake finger manufactured from commonly available materials. Importantly our attackers were novices to producing the fingers and were also constrained by time. Our study shows the relative ease that modern devices can be attacked and the material combinations that lead to these attacks

Open-Source Finger Vein Acquisition Device for Biometric Applications

Vascular biometrics, including finger vein recognition, is growing both in terms of academic research and industrial systems deployed in real life. In this paper, we present an effective, portable, low-cost, and fully open-source finger vein data acquisition system: the schematics, printed circuit board (PCB) Gerber and drill files, 3D models for the 3D printed case, device software and control, and data collection software for PC, are made available. We have collected a test database consisting of 30 people/180 class finger vein images, and obtained preliminary recognition results with HTER = 2.8%, using stateof-the-art Maximum Curvature/Miura Match pipeline, which is a promising indicator for the viability of the proposed concept and further research

Fast cross-correlation based wrist vein recognition algorithm with rotation and translation compensation

Most of the research on vein biometrics addresses the problems of either palm or finger vein recognition with a considerably smaller emphasis on wrist vein modality. This paper paves the way to a better understanding of capabilities and challenges in the field of wrist vein verification. This is achieved by introducing and discussing a fully automatic cross-correlation based wrist vein verification technique. Overcoming the limitations of ordinary cross-correlation, the proposed system is capable of compensating for scale, translation and rotation between vein patterns in a computationally efficient way. Introduced comparison algorithm requires only two cross-correlation operations to compensate for both translation and rotation, moreover the well known property of log-polar transformation of Fourier magnitudes is not involved in any form. To emphasize the veins, a two-layer Hessian-based vein enhancement approach with adaptive brightness normalization is introduced, improving the connectivity and the stability of extracted vein patterns. The experiments on the publicly available PUT Vein wrist database give promising results with FNMR of 3.75% for FMR of 0.1%. In addition we make this research reproducible providing the source code and instructions to replicate all findings in this work