Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Decision Support Systems in Neurosurgery: Current Applications and Future Directions.

Sensors (Basel, Switzerland)·2025
Same author

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation.

Sensors (Basel, Switzerland)·2024
Same author

Siamese Neural Network for Keystroke Dynamics-Based Authentication on Partial Passwords.

Sensors (Basel, Switzerland)·2023
Same author

Intelligent Mobile Wireless Network for Toxic Gas Cloud Monitoring and Tracking.

Sensors (Basel, Switzerland)·2021

相关实验视频

Updated: Jul 20, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K

在线签名生物识别用于移动设备

Katarzyna Roszczewska1, Ewa Niewiadomska-Szynkiewicz1

  • 1Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
概括

移动设备可以通过使用深度神经网络的手写签名有效地验证用户身份. 这种新的方法实现了随机和熟练伪造的低错误率,提高了生物识别安全性.

科学领域:

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

背景情况:

  • 生物识别身份验证对于身份验证至关重要.
  • 手写签名验证是一个具有挑战性的生物识别模式.
  • 移动设备为生物识别数据采集提供了一个方便的平台.

研究的目的:

  • 评估手机获取的手写签名对用户身份验证的有效性.
  • 引入使用移动传感器数据的新型在线签名验证方法.
  • 研究深度神经网络在签名验证中的性能.

主要方法:

  • 使用移动应用程序收集在线签名数据 (坐标,压力).
  • 开发并应用了卷积神经网络 (CNN) 模型:SigNet,SigNetExt和VGG-16.
  • 在MobiBits数据库子集上进行了封闭式验证实验.

主要成果:

  • 在随机伪造中实现了0.63%的等错率 (EER).
  • 在熟练造方面实现了6.66%的EER.
  • 证明了用于移动签名验证的深度神经网络的成功应用.
关键词:
人工智能的人工智能是人工智能.生物识别信息 生物识别信息卷积神经网络是一种卷积神经网络.移动设备 移动设备 移动设备签名识别方式 签名识别方式

更多相关视频

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
06:48

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Published on: June 7, 2024

1.2K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

434

相关实验视频

Last Updated: Jul 20, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research
06:48

Author Spotlight: Advancements in 3D Optical Imaging for Comprehensive Body Composition Assessment in Modern Research

Published on: June 7, 2024

1.2K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

434

结论:

  • 深度神经网络架构对于在线手写签名验证是有效的.
  • 移动设备适合捕获可靠的签名数据用于身份验证.
  • 拟议的方法为安全和方便的生物识别身份验证提供了一个有希望的解决方案.