$\textit{sweet}$- An Open Source Modular Platform for Contactless Hand Vascular Biometric Experiments
This addresses hygiene concerns in biometric systems for users in sensitive environments, but it is incremental as it builds on existing vascular biometric methods with new hardware and data.
The authors tackled the problem of contact-based hand vascular biometric systems by developing a contactless sensor platform called sweet, which supports multiple acquisition modalities and collected a dataset from 120 subjects, achieving biometric experimental results for finger-vein recognition.
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.