一种高效的手指静脉识别方法,基于改进的轻量级移动网络.
Xuhui Zhang1, Yuxi Liu1, Yixin Yan1
1School of Measurement and Control Technology and Communication Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了一种高效的指纹静脉识别系统,使用轻质卷积神经网络 (LCNN). 该方法实现了高精度和快速处理,使其适合实时生物识别安全.
科学领域:
- 生物识别和模式识别技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 手指静脉识别是一种强大的生物识别技术,用于身份认证.
- 传统方法在不同的条件下面临特征表示和效率方面的挑战.
- 实时应用需要轻量级和高效的生物识别系统.
研究的目的:
- 开发一种高效的指纹静脉识别方法,使用轻量级卷积神经网络 (LCNN).
- 提高生物识别身份验证的特征表示和计算效率.
- 为了实现手指静脉识别系统的实时和嵌入式部署.
主要方法:
- 实现了一个轻量级卷积神经网络 (LCNN) 架构.
- 集成了一个多阶段的图像预处理管道:静脉检测,无雾化和纹理增强.
- 在LCNN框架内使用紧特征建模.
主要成果:
- 在SDUMLA-HMT数据集上达到97.1%的高识别准确度,在Lab-Vein数据集上达到98.3%.
- 证明了参数复杂性和计算成本的显著降低.
- 实现了平均推断时间12.6ms,证实了强大的实时能力.
结论:
- 拟议的基于LCNN的手指静脉识别方法提供了卓越的准确性和效率.
- 该系统适用于实时应用程序和嵌入式部署.
- 这种方法推进了轻量级的生物识别系统,具有准确性和速度的平衡.
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