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Open AccessArticle

Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset

Department of Computer Sciences, University of Salzburg, Jakob-Haringer-Str. 2, 5020 Salzburg, Austria
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Author to whom correspondence should be addressed.
Sensors 2019, 19(22), 5014; https://doi.org/10.3390/s19225014
Received: 15 October 2019 / Revised: 8 November 2019 / Accepted: 13 November 2019 / Published: 17 November 2019
(This article belongs to the Special Issue Biometric Systems)
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. View Full-Text
Keywords: finger vein recognition; hand vein recognition; contactless acquisition device; public vascular pattern dataset; biometric recognition performance evaluation finger vein recognition; hand vein recognition; contactless acquisition device; public vascular pattern dataset; biometric recognition performance evaluation
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Kauba, C.; Prommegger, B.; Uhl, A. Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset. Sensors 2019, 19, 5014.

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