Finger vein biometrics is becoming more and more popular. However, longitudinal finger rotation, which can easily occur in practical applications, causes severe problems as the resulting vein structure is deformed in a non-linear way. These problems will become even more important in the future, as finger vein scanners are evolving toward contact-less acquisition. This paper provides a systematic evaluation regarding the influence of longitudinal rotation on the performance of finger vein recognition systems and the degree to which the deformations can be corrected. It presents two novel approaches to correct the longitudinal rotation, one based on the known rotation angle. The second one compensates the rotational deformation by applying a rotation correction in both directions using a pre-defined angle combined with score level fusion and works without any knowledge of the actual rotation angle. During the experiments, the aforementioned approaches and two additional are applied: one correcting the deformations based on an analysis of the geometric shape of the finger and the second one applying an elliptic pattern normalization of the region of interest. The experimental results confirm the negative impact of longitudinal rotation on the recognition performance and prove that its correction noticeably improves the performance again.

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Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated capturing device design is important for good image quality and, furthermore, at a decent recognition rate. We propose a novel contactless capturing device design, including technical details of its individual parts. Our capturing device is suitable for finger and hand vein image acquisition and is able to acquire palmar finger vein images using light transmission as well as palmar hand vein images using reflected light. An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well.