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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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相关实验视频

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Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation BiFC-PALM
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基于主要线路特征的手指纹识别.

Hongxia Wang1, Teng Lv1

  • 1School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, Anhui, China.

PeerJ. Computer science
|September 24, 2025
PubMed
概括

使用宽线提取 (WLE) 和Gabor过器的新型数据增强方法提高了手掌纹识别的准确性. 一个新的层视觉变压器 (LViT) 模型实现了最先进的结果,改善了抗噪声和更少的训练代.

科学领域:

  • 生物识别信息 生物识别信息
  • 计算机视觉 计算机视觉
  • 机器学习 机器学习

背景情况:

  • 由于各种成像设备,手指纹识别对于现代安全至关重要.
  • 传统的方法难以有效地从手掌纹中提取主要线条特征.
  • 掌印中的细线可以引入噪音并阻碍准确的特征提取.

研究的目的:

  • 引入一种新的数据增强方法,以改进手掌纹识别.
  • 为增强功能提取提出一个新的层视觉变压器 (LViT) 设计范式.
  • 评估拟议的方法的性能,稳定性和效率.

主要方法:

  • 使用宽线提取 (WLE) 过器,根据方向和宽度提取主要的手掌纹线.
  • 在WLE后应用了加博波器来净化特征并消除细线噪声.
  • 开发了具有独特阻断策略的LViT,用于多层次的特征捕获和融合结果.
  • 在多个数据库上评估性能,包括PolyU II,IIT Delhi,XINHUA和NTU-CP-V1.

主要成果:

  • 数据增强改善了四种视觉变压器 (ViT) 模型的识别率,在新华数据库中增加了32.9%.
  • 通过有效地利用融合了本地和全球特征,LViT在多个数据库上取得了最先进的结果.
关键词:
数据增强数据增强层层的视觉变压器多个补丁的多个补丁掌印识别功能 掌印识别功能宽线提取 宽线提取

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  • LViT表现出优异的耐噪声概括能力,在模拟的真实噪声条件下保持稳定的性能.
  • 与传统方法相比,LViT需要更少的培训代.

    • 结论:

    • 拟议的数据增强方法显著提高了手掌纹识别的准确性.
    • LViT为掌纹识别提供了一个有前途的新范式,实现了卓越的性能和效率.
    • 开发的方法表现出强大的抗噪强度,使其适合于现实世界的应用.