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@ARTICLE{Geissbuhler_ARXIV_2024,
         author = {Geissbuhler, David and Bhattacharjee, Sushil and Kotwal, Ketan and Clivaz, G. and Marcel, S{\'{e}}bastien},
       projects = {Idiap, Innosuisse CANDY},
          month = apr,
          title = {SWEET - An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments},
        journal = {arXiv},
           year = {2024},
            url = {https://arxiv.org/abs/2404.09376},
            doi = {https://doi.org/10.48550/arXiv.2404.09376},
       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).}
}