ARTICLE Geissbuhler_ARXIV_2024/IDIAP SWEET - An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments Geissbuhler, David Bhattacharjee, Sushil Kotwal, Ketan Clivaz, G. Marcel, Sébastien arXiv 2024 https://arxiv.org/abs/2404.09376 URL https://doi.org/10.48550/arXiv.2404.09376 doi 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).