Behavioural biometrics is becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which cannot be exploited to extract behavioural biometric data. In this study, the authors give the reader an overview of mobile device behavioural biometric data and how this data is used in experiments, especially examining papers that introduce new datasets. They 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, 40 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. They offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. They 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.

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 state-of-the-art Maximum Curvature/Miura Match pipeline, which is a promising indicator for the viability of the proposed concept and further research.

One of the less-discussed topics in biometrics is the use of database protocols and other experimental setup specifics. That is why, in this paper, we study how different protocols impact the recognition results. To do that, we use the vascular biometrics UTFVP finger vein database.

Vision-based traffic monitoring recovers metric quantities such as inter-vehicle distances by mapping the image to the ground plane through a planar homography. In deployed systems, this homography is often fitted from ground control points whose world coordinates are obtained from satellite orthoimagery, where each coordinate is uncertain by about one image pixel. The impact of this control-point coordinate error on the measured distance has not been quantified for a single fixed camera. This paper evaluates a 180-degree panoramic camera (AXIS P3807-PVE) on two adjacent ground-plane regions, each referenced to RTK-surveyed control points and mapped by a separate homography. Control-point coordinates are perturbed with isotropic Gaussian noise, the homography is refitted, and the resulting change in inter-point distance between held-out evaluation points is measured against the unperturbed fit. The distance error is dominated by random scatter rather than systematic bias, and the scatter grows approximately linearly with the coordinate noise, at about 0.7 to 0.8 times the per-coordinate standard deviation within the tested range. The results indicate that distance error can be predicted from control-point coordinate accuracy, and that relative precision of those coordinates matters more than absolute georeferencing.