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

5.4K
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...
5.4K
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.8K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.8K
Force Classification01:22

Force Classification

2.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.6K
Functional Classification of Joints01:09

Functional Classification of Joints

8.5K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
8.5K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

735
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...
735
Association Areas of the Cortex01:21

Association Areas of the Cortex

10.1K
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,...
10.1K

You might also read

Related Articles

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

Sort by
Same author

The Noor Project: fair transformer transfer learning for autism spectrum disorder recognition from speech.

Frontiers in digital health·2025
Same author

Self-Supervised Video-Centralised Transformer for Video Face Clustering.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

End-to-End Video-to-Speech Synthesis Using Generative Adversarial Networks.

IEEE transactions on cybernetics·2022
Same author

FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Speech-Driven Facial Animations Improve Speech-in-Noise Comprehension of Humans.

Frontiers in neuroscience·2022
Same author

Personalized machine learning for robot perception of affect and engagement in autism therapy.

Science robotics·2020

Related Experiment Video

Updated: Mar 8, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

233

Joint Facial Action Unit Detection and Feature Fusion: A Multi-conditional Learning Approach.

Stefanos Eleftheriadis, Ognjen Rudovic, Maja Pantic

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 24, 2017
    PubMed
    Summary

    This study introduces a new model for analyzing facial expressions by jointly detecting action units and fusing facial features. This approach improves accuracy in automated facial expression analysis for various applications.

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    233

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Human-Computer Interaction

    Background:

    • Automated facial expression analysis is crucial for fields like marketing and clinical diagnosis.
    • Facial expressions are composed of co-occurring facial muscle activations (action units).
    • Existing methods often neglect dependencies between action units and facial features.

    Purpose of the Study:

    • To develop a novel model for simultaneous facial feature fusion and joint action unit detection.
    • To address limitations in current methods that fail to exploit action unit dependencies.

    Main Methods:

    • Proposed a multi-conditional latent variable model for generative feature fusion and discriminative action unit detection.
    • Employed a low-dimensional shared subspace for feature fusion.
    • Utilized Bayesian learning with Monte Carlo sampling to reduce parameters and prevent overfitting.

    Main Results:

    • The proposed method outperforms existing purely discriminative or generative approaches.
    • Simultaneous feature fusion and joint action unit learning significantly improve performance.
    • Validated on posed and spontaneous facial expression datasets (CK+, DISFA, Shoulder-pain).

    Conclusions:

    • The novel model effectively integrates feature fusion and joint action unit detection.
    • This approach enhances the accuracy of automated facial expression analysis.
    • Demonstrated superior performance compared to state-of-the-art methods on multiple datasets.