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相关概念视频

Veins of Upper Limbs01:17

Veins of Upper Limbs

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The human circulatory system, a marvel of biological engineering, is a complex network of vessels that transport blood throughout the body. Among these, the veins responsible for carrying blood from the upper limbs are divided into two categories: deep and superficial.
The deep venous system is primarily composed of the ulnar and radial veins. The ulnar vein, which drains the fingers through the superficial palmar venous arches, and the radial vein, which serves the palms via the deep palmar...
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有效的多手指静脉识别使用层级渐进的移动网络微调和密集头概率的罗网络.

Alaa S Alaerjan1, Ayman Mohamed Mostafa2, Alshimaa Abdelraof Mahmoud3

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, 72388, Sakaka, Saudi Arabia.

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概括

这项研究引入了一种用于指脉识别的新型深度学习框架,可以在资源有限的设备上实现多指身份验证. 这种高效的系统实现了最先进的准确性,克服了以前的部署挑战.

关键词:
生物识别身份验证的真实性识别手指静脉的功能层层地进行逐步微调.轻量级的深度学习是轻量级的.移动网络 (MobileNet) 是一个移动网络.

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科学领域:

  • 生物识别和模式识别技术
  • 深度学习用于安全.
  • 嵌入式系统安全 嵌入式系统安全

背景情况:

  • 指脉识别是一种安全的生物识别模式,但在资源有限的设备中面临着计算成本和单指限制的挑战.
  • 现有的指脉识别深度学习模型通常是计算密集的,在招生中缺乏灵活性.
  • 单指注册的刚性限制了用户的便利性和系统的适应性.

研究的目的:

  • 开发一个计算效率高,灵活的深度学习框架来识别手指静脉.
  • 在资源有限的设备上实现多指身份验证,而不会影响准确性或速度.
  • 为了应对高计算成本和单指注册在当前手指静脉系统中的挑战.

主要方法:

  • 这是一个两阶段的深度学习框架,它结合了轻量级的,适应性的MobileNet功能提取器和密集头概率的罗人 (DHPS) 匹配器.
  • 层层地解技术,以优化特征提取器,在模型紧性和判别力之间保持平衡.
  • DHPS匹配器使用通过二进制交叉来优化校准的概率输出,取代传统的基于保证金的损失.

主要成果:

  • 在三个公开的指脉数据集 (FV-USM,UTFVP,VERA) 上实现了最先进的性能,低等错误率 (EER) 为0.002,0.067和0.075.
  • 在各自的测试组中获得了高F1分数99.8%,95.6%和91.3%,证明了强大的准确性.
  • 紧的模型有助于快速推断,使其适合嵌入式平台.

结论:

  • 拟议的框架显著提高了手指静脉生物识别的准确性,灵活性和效率.
  • 这种进步克服了现实世界部署的主要障碍,特别是嵌入式系统.
  • 预训练的特征提取器将公开发布,以促进对高效生物识别系统的进一步研究.