Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

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

Object-Aware Computational Integral Imaging for Improved Object Depth Estimation and Stereo Matching Training.

Sensors (Basel, Switzerland)·2026

Same author

Ultrafast carrier dynamics and electronic properties of PtSe<sub>2</sub>/MoSe<sub>2</sub> and WSe<sub>2</sub> 2D TMDC layered structures on mica: combined THz spectroscopy and DFT study.

RSC advances·2026

Same author

Development of Polarization-Insensitive THz-to-IR Converters for Low-IR-Signature Target Detection and Imaging.

Sensors (Basel, Switzerland)·2024

Same author

Emotion Classification Based on Pulsatile Images Extracted from Short Facial Videos via Deep Learning.

Sensors (Basel, Switzerland)·2024

Same author

Atmospheric Turbulence Degraded Video Restoration with Recurrent GAN (ATVR-GAN).

Sensors (Basel, Switzerland)·2023

Same author

3D Object Detection via 2D Segmentation-Based Computational Integral Imaging Applied to a Real Video.

Sensors (Basel, Switzerland)·2023

Search research articles

相关实验视频

Updated: Feb 28, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

隐藏面部分析和面部重建通过多任务方法和跨模态蒸在特拉赫兹成像中进行.

Noam Bergman¹, Ihsan Ozan Yildirim², Asaf Behzat Sahin³

¹Department of Electro-Optical Engineering, School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 1 Ben-Gurion Blvd, Beer Sheva 8410501, Israel.

Sensors (Basel, Switzerland)

|February 27, 2026

概括

这项研究引入了一种新的多任务学习网络,用于特拉赫兹 (THz) 成像,以改善隐藏的面部识别和重建. 交叉模式的方法增强了仅THz的生物识别,克服了稀疏性和噪声限制.

关键词:

一个THz面部重建.跨模式的融合融合.深度学习是一种深度学习.面部生物识别技术知识的蒸知识的蒸.多任务学习是多任务学习.泰拉赫兹成像技术的成像

更多相关视频

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

相关实验视频

Last Updated: Feb 28, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

科学领域:

生物识别和成像技术
人工智能和机器学习
信号处理信号处理

背景情况:

太赫兹 (THz) 成像使得立即生物识别成为可能,但面临着稀疏性,低分辨率和高噪音等挑战.
现有的THz生物识别系统在数据限制和图像质量方面扎.

研究的目的:

开发一个统一的多任务学习 (MTL) 网络,用于增强的特拉赫兹 (THz) 面部生物识别.
用THz数据改进隐藏面部验证,面部姿势分类和生成重建.
探索跨模式的知识蒸,以增强只有THz的学生模型.

主要方法:

一个新的基于编码器的U-Net类多任务学习 (MTL) 网络被设计用于隐藏的THz面部数据.
该网络同时处理了面部验证,姿势分类和生成重建任务.
采用了交叉模式的教师-学生方法,使用可见光谱数据指导在蒸过程中仅使用THz的学生模型.

主要成果:

统一的MTL网络在具有挑战性的THz面部图像数据集上表现出非常成功的性能.
与单一模式基线相比,交叉模式蒸学生模型显示了潜在空间分离能力的改善.
仅THz和蒸模型都保持了高保真度,从隐藏的输入中生成未隐藏的面孔.

结论:

拟议的MTL网络有效地解决了THz面部生物识别方面的局限性.
跨模式蒸为增强仅THz生物识别系统提供了一个可行的策略.
开发的模型显示了使用特拉赫兹成像进行强大的隐藏生物识别的前景.