一个开源的模块化平台,用于无接触式手动血管生物识别实验
David Geissbühler1, Sushil Bhattacharjee1, Ketan Kotwal1
1Idiap Research Institute, 1920 Martigny, Switzerland.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了无接触式血管生物识别平台,用于卫生地识别手脉和手掌纹. 该系统可实现非接触式数据采集,提高敏感环境中的可用性.
科学领域:
- 生物识别和模式识别
- 计算机视觉
- 人与计算机的交互
背景情况:
- 传统的静脉识别系统需要身体接触,
- 无接触生物识别对于要求高卫生标准的应用至关重要.
- 目前用于血管生物识别的无接触解决方案有限.
研究的目的:
- 开发和评估一个新的无接触血管生物识别传感器平台.
- 为了实现手部血管和表面特征的多模式数据采集.
- 创建一个新的数据集用于手指静脉识别研究.
主要方法:
- 开发了支持多光谱近红外 (NIR),RGB颜色,立体视觉 (SV) 和光度立体 (PS) 模式的"甜蜜"无接触传感器平台.
- 收集了120名受试者的手指,手掌和手腕血管数据.
- 进行专注于指脉识别 (FVR) 的实验,并探索多式融合技术.
主要成果:
- 无接触平台成功获取手指,手掌和手腕的血管和表面数据.
- 实验结果显示了该平台的生物识别应用潜力,特别是FVR.
- 分析包括将多种获取方式的数据合并的有效性.
结论:
- "甜蜜"平台为血管生物识别提供了可行的无接触解决方案,解决了卫生问题.
- 新收集的数据集和开源资源将推动无接触生物识别的研究.
- 多模式融合有望提高生物识别系统的准确性和稳定性.
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