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

• URL: http://arxiv.org/abs/2404.09376v2
• Date: Wed, 11 Sep 2024 14:22:45 GMT
• Title: $\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
• Authors: David Geissbühler, Sushil Bhattacharjee, Ketan Kotwal, Guillaume Clivaz, Sébastien Marcel,
• Abstract summary: We present a contactless vascular biometrics sensor platform named sweet. It 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)
• Score: 19.054919189900303
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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