Geissbühler David, Bhattacharjee Sushil, Kotwal Ketan, Clivaz Guillaume, Marcel Sébastien
Idiap Research Institute, 1920 Martigny, Switzerland.
Ecole des Sciences Criminelles, Université de Lausanne, 1015 Lausanne, Switzerland.
Sensors (Basel). 2025 Aug 12;25(16):4990. doi: 10.3390/s25164990.
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 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 collected a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects. We present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities. 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.
当前的手指静脉或手掌静脉识别系统通常要求受试者与设备直接接触。在卫生至关重要的环境中,这可能会成为问题。在这项工作中,我们展示了一个名为的非接触式血管生物特征识别传感器平台,该平台可用于手部血管生物特征识别研究(手腕、手掌和手指静脉)以及诸如掌纹等表面特征。它支持多种采集模式,如多光谱近红外(NIR)、RGB颜色、立体视觉(SV)和光度立体(PS)。使用这个平台,我们收集了一个由120名受试者的手指、手掌和手腕血管数据组成的数据集。我们展示了生物特征识别实验结果,重点是手指静脉识别(FVR)。最后,我们讨论了多种模式的融合。采集软件、部分硬件设计、新的FV数据集以及我们实验的源代码都已公开提供,供研究使用。