Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

247
Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
247
Physiology of Emotion01:20

Physiology of Emotion

1.5K
The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
1.5K
Emotional Expression01:26

Emotional Expression

383
Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
383
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

253
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...
253
Introduction to Motivation and Emotion01:29

Introduction to Motivation and Emotion

515
Motivation is a multifaceted process that drives behavior toward fulfilling various physiological or psychological needs. This process involves initiating, guiding, and maintaining specific actions influenced by internal and external factors. For example, when someone feels hungry while watching television, hunger is a motivator, prompting the individual to get up, walk to the kitchen, and find something to eat. In this instance, hunger initiates and sustains the behavior necessary to meet the...
515
Cognitive Theories: Schachter-Singer Theory of Emotion01:20

Cognitive Theories: Schachter-Singer Theory of Emotion

611
Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
Physiological Arousal and Cognitive Labeling
According to this theory, when an individual experiences...
611

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Black-Winged Kite Algorithm Integrating Opposition-Based Learning and Quasi-Newton Strategy.

Biomimetics (Basel, Switzerland)·2026
Same author

Arctic Puffin Optimization Algorithm Integrating Opposition-Based Learning and Differential Evolution with Engineering Applications.

Biomimetics (Basel, Switzerland)·2025
Same author

FineTea: A Novel Fine-Grained Action Recognition Video Dataset for Tea Ceremony Actions.

Journal of imaging·2024
See all related articles

Related Experiment Video

Updated: Sep 18, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.2K

Advances in Video Emotion Recognition: Challenges and Trends.

Yun Yi1,2, Yunkang Zhou1, Tinghua Wang1,2

  • 1School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.

Sensors (Basel, Switzerland)
|June 27, 2025
PubMed
Summary
This summary is machine-generated.

This study explores video emotion recognition (VER), a field combining affective computing and computer vision. It reviews current VER methods, identifies key challenges, and proposes future research directions for improved emotion detection in videos.

Keywords:
algorithmsdatasetsemotional representationpsychological modelsvideo emotion recognition

More Related Videos

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

4.1K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.6K

Related Experiment Videos

Last Updated: Sep 18, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

9.2K
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

4.1K
Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.6K

Area of Science:

  • Affective Computing
  • Computer Vision
  • Human-Computer Interaction

Background:

  • Video emotion recognition (VER) analyzes viewer emotions from video content.
  • Applications include video recommendation, HCI, and intelligent education.
  • VER is grounded in psychological models of emotion.

Purpose of the Study:

  • To provide a comprehensive review of VER.
  • To analyze existing datasets, evaluation metrics, and algorithms.
  • To identify current challenges and propose future research directions.

Main Methods:

  • Analysis of psychological models foundational to VER.
  • Review and categorization of VER algorithms.
  • Comparison and analysis of classic methods across four datasets.
  • Identification of challenges in emotional representation, datasets, and multimodal integration.

Main Results:

  • Key challenges identified: emotional representation gaps, dataset limitations, and multimodal fusion.
  • Classic VER methods were compared on standard datasets.
  • The study highlights the need for advanced neural networks and fusion strategies.

Conclusions:

  • Future research should focus on advanced neural networks and multimodal fusion.
  • Developing high-quality emotional representations and active learning strategies is crucial.
  • Addressing current challenges will advance the field of video emotion recognition.