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

Muscles for Facial Expressions01:14

Muscles for Facial Expressions

5.6K
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...
5.6K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

845
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...
845
Association Areas of the Cortex01:21

Association Areas of the Cortex

10.4K
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,...
10.4K

You might also read

Related Articles

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

Sort by
Same author

Ligand Geometry Regulated Architecture of Ultra-Microporous Flexible Guanidinium-Based Hydrogen-Bonded Organic Frameworks for Highly Selective Nitrous Oxide/Nitrogen Separation.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Network-Texture-Induced Uniform Nucleation: Controllable Preparation and Application of High-Performance CsPbI<sub>3</sub> Nanocrystals in Al<sup>3+</sup>/Gd<sup>3+</sup> Co-Doped Glass.

Inorganic chemistry·2026
Same author

Anomalous luminescence properties in Dy<sup>3+</sup>-doped Bi<sub>2</sub>O<sub>3</sub>-B<sub>2</sub>O<sub>3</sub>-SiO<sub>2</sub> glasses at high silver concentrations.

Applied optics·2026
Same author

Is Coffee Consumption Associated With Increased Risk of Atrial Fibrillation: A Systematic Review and a Meta-Analysis.

Pacing and clinical electrophysiology : PACE·2026
Same author

Retinal Microvascular Dysfunction Reflects Vascular and Alzheimer's-Related Pathology in Dementia With Lewy Bodies.

CNS neuroscience & therapeutics·2026
Same author

Design of phosphors in glass doped with silver nanocrystals (Sr,Ca)AlSiN<sub>3</sub>:Eu<sup>2+</sup> for sunlight-like lighting excited by violet light chips.

Applied optics·2026

Related Experiment Video

Updated: Mar 24, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

16.5K

Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation.

Yimo Guo, Guoying Zhao, Matti Pietikainen

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |March 9, 2016
    PubMed
    Summary

    This study introduces a novel dynamic facial expression recognition method using longitudinal groupwise registration. The new approach achieves higher recognition rates by integrating spatial and temporal information, outperforming existing methods.

    More Related Videos

    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

    10.0K
    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

    938

    Related Experiment Videos

    Last Updated: Mar 24, 2026

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
    10:28

    Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

    Published on: July 24, 2019

    16.5K
    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

    10.0K
    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

    938

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Biomedical Image Analysis

    Background:

    • Dynamic facial expression recognition (DFER) is crucial for human-computer interaction and affective computing.
    • Existing DFER methods often struggle with inter-subject variations and capturing subtle temporal dynamics.

    Purpose of the Study:

    • To propose a novel DFER method formulated as a longitudinal groupwise registration problem.
    • To enhance recognition accuracy by modeling subject-specific facial feature movements and building a population-level facial expression atlas.

    Main Methods:

    • A diffeomorphic growth model describes subject-specific facial feature movements.
    • Sparse groupwise image registration constructs a salient longitudinal facial expression atlas.
    • Sparse representation integrates spatial appearance and temporal topological evolution for recognition.

    Main Results:

    • The proposed framework was evaluated on five diverse databases (CK+, MMI, FERA, AFEW, UNBC-McMaster).
    • Experimental results show consistently higher recognition rates compared to state-of-the-art DFER methods.
    • The method effectively suppresses bias from inter-subject facial variations.

    Conclusions:

    • The proposed longitudinal groupwise registration framework offers a robust approach to DFER.
    • Integrating spatial and temporal information significantly improves recognition performance.
    • This method demonstrates potential for applications in emotion recognition and pain monitoring.