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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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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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相关实验视频

Updated: Jul 25, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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基于卷积神经网络的儿童识别系统使用非接触式指纹.

Kanchana Rajaram1, N G Bhuvaneswari Amma2, S Selvakumar3,4

  • 1Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu 603 110 India.

International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management
|June 26, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了Child-CLEF,这是一个用于使用卷积神经网络 (CNN) 的儿童的非接触式指纹识别系统. 该系统提高了图像质量,并提取了用于准确识别的特征,优于现有方法.

关键词:
生物识别安全 生物识别安全孩子的指纹 孩子的指纹无接触式指纹指纹卷积神经网络是一种卷积神经网络.指纹识别指纹识别图像采集 图像采集 图像采集

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科学领域:

  • 生物识别和模式识别技术
  • 计算机视觉和图像处理
  • 人工智能在安全方面的应用

背景情况:

  • 指纹识别对于安全至关重要,但儿童未成熟的指纹和难以获取图像存在挑战.
  • 随着COVID-19的爆发,人们越来越需要无接触式生物识别解决方案,尤其是儿童.
  • 现有系统正在努力应对儿科指纹数据的独特挑战.

研究的目的:

  • 为儿童开发一个强大而准确的无接触指纹识别系统.
  • 解决儿科生物识别传统指纹识别方法的局限性.
  • 利用深度学习来增强儿童指纹识别.

主要方法:

  • 一个基于卷积神经网络 (CNN) 的系统,Child-CLEF,是使用CLCF数据集开发的.
  • 采用混合图像增强技术来提高获得的指纹图像的质量.
  • 使用儿童-CLEF网络模型提取微小的特征,然后通过匹配算法进行识别.

主要成果:

  • 与现有的指纹识别系统相比,Child-CLEF系统表现出卓越的性能.
  • 该系统在自我捕获和公共数据集上实现了高精度和低等错误率 (EER).
  • 使用手机进行非接触式采集,对儿童的指纹有效.

结论:

  • 拟议的儿童-CLEF系统为儿童的无接触指纹识别提供了一个有希望的解决方案.
  • 该研究验证了CNN和图像增强用于儿科生物识别的有效性.
  • 这项研究有助于为儿童提供安全和非侵入性识别方法,特别是在敏感的环境中.