TY - JOUR
AU1 - Geissbühler, David
AU2 - Bhattacharjee, Sushil
AU3 - Kotwal, Ketan
AU4 - Clivaz, Guillaume
AU5 - Marcel, Sébastien
AB - Abstract:Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collect a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects and develop a powerful 3D pipeline for the pre-processing of this data. We then present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities, such palm-vein combined with palm-print biometrics. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.
TI - $\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
JF - Computing Research Repository
DO - 10.48550/arxiv.2404.09376
DA - 2024-09-11
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/textit-sweet-an-open-source-modular-platform-for-contactless-hand-zPnrImxwzR
VL - 2024
IS - 2404
DP - DeepDyve
ER -