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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

937
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...
937

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相关实验视频

Updated: Jul 18, 2025

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
07:18

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在空中手势签名识别:一个iHGS数据库采集协议.

Wee How Khoh1, Ying Han Pang1, Hui Yen Yap1

  • 1Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia.

F1000Research
|August 21, 2023
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种用于空中手势签名识别的新方法,创建了一个公开可访问的数据库. 该系统实现了人类分类的高精度,并证明了对各种伪造攻击的稳定性.

关键词:
动态签名 动态签名伪造者攻击攻击手势识别 手势识别手势 签名 手势 签名手势签名数据库 手势签名数据库图像处理 图像处理

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Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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相关实验视频

Last Updated: Jul 18, 2025

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科学领域:

  • 计算机科学 计算机科学
  • 生物识别信息 生物识别信息
  • 人与计算机的交互

背景情况:

  • 手势识别正在进步,在人机交互 (HCI) 中用于非接触式识别.
  • 在空中手势签名识别可通过独特的手动识别用户.
  • 在这个领域,缺乏公开可访问的数据库和详细的协议.

研究的目的:

  • 为了展示收集空中手势签名数据库的程序.
  • 提供一个参考数据库,用于在手势识别领域的评估.
  • 建立一个用于获取和处理空中手势签名的协议.

主要方法:

  • 在两个会议中收集了100名志愿者的签名,生成了真实和伪造的数据集.
  • 使用微软 Kinect 传感器摄像头进行签名采集.
  • 预处理的数据与手部本地化和细分,然后以矢量为基础的特征提取.

主要成果:

  • 在使用多类支持向量机 (SVM) 的分类中获得了97.43%的准确性.
  • 在系统稳定性分析中表现出较低的错误率:随机伪造2.41%和熟练伪造5.07%.
  • 验证了提取特征的有效性,以区分真伪签名.

结论:

  • 在空中手势签名是可行的可靠的人类分类.
  • 开发的系统表现出对随机和熟练的伪造攻击的强度.
  • 创建的数据库是未来生物识别研究的宝贵资源.