$\textitsweet$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
<|reference_start|>$\textitsweet$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments: 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.<|reference_end|>
@article{geissbühler2024$\textit{sweet}$-,
    title={$\textit{sweet}$- An Open Source Modular Platform for Contactless Hand
  Vascular Biometric Experiments},
    author={David Geissb"uhler, Sushil Bhattacharjee, Ketan Kotwal, Guillaume
  Clivaz, S'ebastien Marcel},
    journal={arXiv preprint arXiv:2404.09376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09376},
    primaryClass={cs.CV}
}