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

Association Areas of the Cortex01:21

Association Areas of the Cortex

4.9K
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:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
4.9K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

119
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
119
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

1.7K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
1.7K

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

Optimizing knee osteoarthritis severity prediction on MRI images using deep stacking ensemble technique.

Scientific reports·2024
Same author

Detection of Alzheimer's Disease Based on Cloud-Based Deep Learning Paradigm.

Diagnostics (Basel, Switzerland)·2023
查看所有相关文章

相关实验视频

Updated: May 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455

深度卷积神经网络架构用于面部情绪识别.

Dayananda Pruthviraja1, Ujjwal Mohan Kumar2, Sunil Parameswaran2

  • 1Information Technology, Manipal Insitute of Technology, Manipal Academy of Higher Education, Bengaluru, Karnataka, India.

PeerJ. Computer science
|February 3, 2025
PubMed
概括

深层卷积神经网络 (DCNNs) 通过提取详细的面部特征来提高面部情绪检测的准确性. 这一进步为人机交互和心理学研究中的应用提供了更高的可靠性.

关键词:
计算机视觉 计算机视觉 计算机视觉深度卷积神经网络是一个深度卷积神经网络.深度学习是一种深度学习.情绪的分类 情绪的分类图像处理 图像处理

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

相关实验视频

Last Updated: May 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

455
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

8.9K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

科学领域:

  • 情感计算是一种情感计算.
  • 计算机视觉 计算机视觉 计算机视觉
  • 机器学习是机器学习.

背景情况:

  • 面部情绪检测对于人机交互,心理研究和情感分析至关重要.
  • 传统的方法在精确性和可靠性方面面临着细微的情感识别的局限性.

研究的目的:

  • 通过使用深 convolutional 神经网络 (DCNNs) 来提高面部情绪检测的准确性和可靠性.
  • 探索DCNN在详细的面部特征提取和强大的情绪识别训练方面的能力.

主要方法:

  • 使用多层DCNN架构,并使用卷积和聚合层进行自动特征提取.
  • 雇员转移学习技术与预先训练有素的DCNN用于有限数据的情绪识别任务.

主要成果:

  • 拟议的DCNN模型在Fer2013Plus数据集上表现出优于传统方法的性能.
  • 在识别各种面部情绪,捕捉微妙和高层次模式方面取得了高准确度.
  • 预先训练的DCNN在有限的标记数据的情绪识别任务中被证明是有效的.

结论:

  • 通过详细的特征提取和强大的训练,DCNN显著提升了面部情绪检测.
  • 开发的模型为情绪识别应用提供了更高的准确性和可靠性.
  • 这项研究有助于以人为中心的技术领域,需要复杂的情感分析.