Jove
Visualize
Contact Us

Related Concept Videos

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

233
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...
233
Association Areas of the Cortex01:21

Association Areas of the Cortex

6.0K
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,...
6.0K
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

2.5K
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...
2.5K
Physiology of Emotion01:20

Physiology of Emotion

1.2K
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...
1.2K
Labeling Emotion01:20

Labeling Emotion

226
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...
226

You might also read

Related Articles

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

Sort by
Same author

Hybrid quantum-classical neural networks for real-time fault detection in power systems.

PloS one·2026
Same author

Harris Hawks-tuned severity-aware YOLOv8 instance segmentation framework for vehicle damage assessment.

Scientific reports·2026
Same author

Real-time deforestation anomaly detection using YOLO and LangChain agents for sustainable environmental monitoring.

Scientific reports·2025
Same author

RETRACTED: Enhanced heart disease diagnosis and management: A multi-phase framework leveraging deep learning and personalized nutrition.

PloS one·2025
Same author

A novel adaptive multi-scale wavelet Galerkin method for solving fuzzy hybrid differential equations.

Scientific reports·2025
Same author

Optimised RFO tuned RF-DETR model for precision urine microscopy for renal and systemic disease diagnosis.

Scientific reports·2025
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
See all related articles
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 Experiment Video

Updated: Aug 27, 2025

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

4.0K

Optimal Facial Feature Based Emotional Recognition Using Deep Learning Algorithm.

Tarun Kumar Arora1, Pavan Kumar Chaubey2, Manju Shree Raman3

  • 1Professor-Department of Applied Sciences and Humanities, ABES Engineering College, Ghaziabad, Uttar Pradesh, India.

Computational Intelligence and Neuroscience
|September 30, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances computer vision for emotion recognition by improving convolutional neural networks (CNNs) and preprocessing techniques. The research accurately identifies seven fundamental emotions from facial expressions, advancing human-computer interaction.

More Related Videos

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.5K
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

9.5K

Related Experiment Videos

Last Updated: Aug 27, 2025

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

4.0K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.5K
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

9.5K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial expression recognition is challenging for computers, despite its ease for humans.
  • Automatic emotion recognition is crucial for human-computer interaction and understanding mental states.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise for complex recognition tasks.

Purpose of the Study:

  • To improve Convolutional Neural Network (CNN) performance for recognizing seven fundamental emotions from facial expressions.
  • To evaluate the impact of various preprocessing techniques on CNN-based emotion recognition.
  • To enhance the detection of facial features and expressions for more accurate emotion identification.

Main Methods:

  • Utilized a deep learning approach employing a Convolutional Neural Network (CNN).
  • Trained and tested the model on a dataset of approximately 32,298 images featuring multiple facial expressions.
  • Implemented preprocessing techniques for noise removal and feature extraction, including a pretraining phase for face detection.

Main Results:

  • The improved CNN technique successfully identified seven fundamental emotions from facial expressions.
  • Preprocessing methods were shown to significantly impact CNN performance in emotion recognition.
  • The study demonstrated effective classification of facial reactions corresponding to the seven emotions of the Facial Acting Coding System (FACS).

Conclusions:

  • The proposed deep learning method using CNNs effectively enhances emotion recognition from facial features.
  • Optimized preprocessing and CNN techniques provide a robust solution for identifying basic human emotions.
  • This advancement contributes to more accurate predictions of mental states and personalized human-computer interactions.