\textit{sweet} -- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments

• DOI: 10.48550/arxiv.2404.09376
• Publication Date: 2024-04-14
• Authors: David Geissbühler; Sushil Bhattacharjee; Ketan Kotwal; Guillaume Clivaz; Sébastien Marcel
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition; I.2, I.4, I.5

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with apparatus. This can be problematic in environments where hygiene is primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which used for hand studies (wrist-, palm- and finger-vein) surface features such as palmprint. It supports several acquisition modalities multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) Photometric (PS). Using collect dataset consisting fingers, palm wrist data 120 subjects develop powerful 3D pipeline pre-processing data. We then biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, discuss fusion multiple modalities, combined palm-print biometrics. The software, parts hardware design, new FV dataset, well source-code our experiments are publicly available research purposes.