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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.3K
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,...
5.3K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

139
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...
139
Vision01:24

Vision

53.1K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
53.1K
Prosopagnosia01:24

Prosopagnosia

156
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
156
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

2.0K
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...
2.0K
Parallel Processing01:20

Parallel Processing

150
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
150

You might also read

Related Articles

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

Sort by
Same author

A Novel Lead Construct to Reduce MRI-Induced RF Heating: Construction, In Vitro Validation, and In Vivo Predictions.

IEEE transactions on bio-medical engineering·2026
Same author

Dual-harmonic generation in a hybrid graphene-lithium niobate nonlinear metasurface.

Optics express·2026
Same author

Avian influenza virus H5N1 and H9N2 subtypes in different birds and humans: Findings from an extensive evaluation.

Open veterinary journal·2026
Same author

Pooling robotic and navigation-assisted systems, bone mineral density omission, and platform-specific pin geometry: critical concerns in the meta-analysis of periprosthetic fractures after tracker pin placement in total knee arthroplasty.

Journal of robotic surgery·2026
Same author

Electron transfer rate of pyrogenic carbon in aquatic environments: Establishment of analytical methods, structural drivers, and roles in pollutant degradation.

Journal of hazardous materials·2026
Same author

A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion Recognition.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Jun 24, 2025

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

9.4K

Facial expression recognition (FER) survey: a vision, architectural elements, and future directions.

Sana Ullah1, Jie Ou1, Yuanlun Xie1

  • 1School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.

Peerj. Computer Science
|June 10, 2024
PubMed
Summary

Facial expression recognition (FER) systems are advancing rapidly, impacting various sectors. This study explores FER technologies, applications, challenges, and future directions to drive innovation in emotion measurement.

Keywords:
Basic & compound emotionsCloud computingComputer visionEmotion recognition technologyInternet of Things (IoT)

More Related Videos

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

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

Published on: December 24, 2015

14.1K
Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.2K

Related Experiment Videos

Last Updated: Jun 24, 2025

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

9.4K
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

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

Published on: December 24, 2015

14.1K
Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
06:53

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation

Published on: March 1, 2017

13.2K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Facial Expression Recognition (FER) is a rapidly evolving field within computer vision.
  • FER systems have diverse applications across education, marketing, health, and transportation.
  • Accurate emotion measurement remains a significant challenge in current FER research.

Purpose of the Study:

  • To provide a comprehensive overview of Facial Expression Recognition (FER) technologies.
  • To discuss the architectural elements, applications, and leading companies in the FER domain.
  • To explore the integration of FER with the Internet of Things (IoT) and Cloud computing, and identify future research directions.

Main Methods:

  • Systematic review utilizing the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) method.
  • In-depth analysis of current FER technologies, including basic and compound emotion recognition.
  • Examination of challenges and future research avenues in FER.

Main Results:

  • The study details the foundational principles and architectural components of FER systems.
  • It highlights the wide-ranging applications and use-cases of FER technology.
  • The research identifies key challenges and proposes future directions for FER advancement.

Conclusions:

  • Overcoming identified challenges in FER is crucial for future breakthroughs.
  • Integrating FER with IoT and Cloud computing offers new possibilities.
  • This research aims to guide future studies in revolutionizing facial expression recognition.