Sweet—An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 16; p. 4990
• Main Authors: Geissbühler, David, Bhattacharjee, Sushil, Kotwal, Ketan, Clivaz, Guillaume, Marcel, Sébastien
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.08.2025; MDPI
• Subjects: Accuracy; Algorithms; Biometrics; Biometry; Biometry - methods; Cameras; Datasets; Deep learning; Experiments; finger-vein recognition; Fingers - blood supply; Hand - blood supply; Health aspects; Humans; multi-spectral imaging; photometric stereo; Sensors; Software; stereoscopy; vascular biometrics; Veins & arteries; Veins - physiology; Wrist - blood supply; United States; New Jersey; Germany; vascular biometrics; finger-vein recognition; multi-spectral imaging; photometric stereo; stereoscopy
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25164990

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 collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. 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.