Fingers Crossed: An Analysis of Cross-Device Finger Vein Recognition

Abstract

Finger veins are one of the emerging biometric traits attracting many researchers in biometric recognition. Despite the growing literature on finger vein recognition, little interest has been shown in the impact of acquisition devices on recognition performance due to the lack of a multisensor finger vein database. This work aims to fill this gap by creating such a database using five different acquisition devices. We then analyze their impact on finger vein recognition performance. The analysis shows two main challenges that decrease recognition performance, namely scaling between device sensors and horizontal shifts between image pairs. The findings of this research give insight into developing more robust finger vein recognition algorithms.