Jove
Visualize
联系我们

相关概念视频

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
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

113
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...
113

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

Rhythm and timing in laughter reveal that human vocal plasticity falls on a hominid continuum.

Communications biology·2026
Same author

Despotism trumps phylogeny in explaining suppression of play among adults in non-human primates.

Biology letters·2026
Same author

The Ontogeny of Vocal Rhythms in a Non-Human Primate.

Developmental science·2026
Same author

The Emergence of a Universal Rhythmic Feature: Simple Models Can Produce Categorical Rhythms.

Annals of the New York Academy of Sciences·2026
Same author

BirdNET: Automated Detection for Monitoring Critically Endangered Lemurs from the Maromizaha Forest.

Integrative zoology·2026
Same author

Ontogeny of Diet and Behavior of a Wild, Critically Endangered Lemur (Indri indri).

American journal of primatology·2025
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

相关实验视频

Updated: May 24, 2025

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

361

在新世界子中使用深度学习量化面部手势.

Filippo Carugati1, Dayanna Curagi Gorio1, Chiara De Gregorio1,2

  • 1Department of Life Sciences and Systems Biology, Università di Torino, Torino, Italy.

American journal of primatology
|February 28, 2025
PubMed
概括

深度学习推进了对灵长类面部手势的自动分析. 无标记物姿势估计可以准确地区分棉花顶塔玛林面部表情在不同的背景下,改进沟通研究.

关键词:
这是一个DeepLabCut.萨古努斯·奥迪普斯 (Saguinus oedipus) 是一个古老的植物.棉花顶部的塔玛林.没有标记的姿势估计估计.灵长类动物的面部表情

更多相关视频

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

359
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

相关实验视频

Last Updated: May 24, 2025

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

361
Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
06:19

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction

Published on: August 16, 2024

359
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.3K

科学领域:

  • 灵长类动物的种族学
  • 计算生物学 计算生物学
  • 动物行为 动物行为

背景情况:

  • 面部手势是灵长类动物多式联络的关键.
  • 目前面部数据提取的方法是手动的,主观的,耗时的.
  • 自动化工具,特别是深度学习,为客观分析提供了潜力.

研究的目的:

  • 通过使用自动化方法,探索棉花塔玛林的面部手势的独特性.
  • 开发和验证用于识别特定面部标志的深度学习模型.
  • 评估模型对与发声和行为相关的面部配置进行分类的能力.

主要方法:

  • 使用无标记物姿势估计算法对被囚禁的棉花鱼的视频录像.
  • 通过手动标记面部地标,开发了一个定制模型.
  • 训练模型预测地标位置并生成距离矩阵.
  • 采用机器学习分类器来区分面部配置.

主要成果:

  • 实现了超过80%的正确分类率,以区分发声和非发声的面部配置.
  • 展示了特定于环境的面部手势识别,在打哈欠,社交活动和休息时具有高准确性.
  • 验证了对灵长类面部交流的自动化分析的潜力.

结论:

  • 无标记的姿势估计显示了对推动灵长类动物多式联络研究的重大前景.
  • 自动化面部手势分析可以有效地区分棉花塔玛林的不同行为背景.
  • 这种方法代表了从视频数据中提取自动行为暗示的关键步骤.