\textit{sweet} -- An Open Source Modular Platform for Contactless Hand
  Vascular Biometric Experiments
David Geissbühler Sushil Bhattacharjee Ketan Kotwal Guillaume Clivaz Sébastien Marcel
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.