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

2.2K
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...
2.2K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

186
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...
186
Functional Classification of Joints01:09

Functional Classification of Joints

4.2K
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...
4.2K
Structural Classification of Joints01:20

Structural Classification of Joints

3.5K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.5K
Force Classification01:22

Force Classification

1.3K
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,...
1.3K
Observational Learning01:12

Observational Learning

209
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
209

You might also read

Related Articles

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

Sort by
Same author

Context-aware sequential course recommendation via conditional variational autoencoders and transformer architectures in IoT-enabled E-learning systems.

Scientific reports·2026
Same author

Higher mitochondrial DNA methylation is associated with increased risk of stroke: a nested case-control study.

Human genetics·2026
Same author

Pseudo-label-free instance screening of non-tumor regions in whole slide images for improved classification and survival prediction.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2026
Same author

Deciphering the ceRNA Network in Alfalfa: Insights into Cold Stress Tolerance Mechanisms.

Biomolecules·2026
Same author

Association between the C-reactive protein to albumin ratio and unplanned readmission in ulcerative colitis: insights from a cohort study.

Frontiers in medicine·2026
Same author

Synergistic photothermal therapy of esophageal cancer using Pt@MOF@PSs nanozymes.

Frontiers in bioengineering and biotechnology·2026

Related Experiment Video

Updated: Jul 18, 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

9.5K

Expression-Guided Deep Joint Learning for Facial Expression Recognition.

Bei Fang1, Yujie Zhao2, Guangxin Han1

  • 1Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi'an 710062, China.

Sensors (Basel, Switzerland)
|August 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient convolutional neural network (CNN) for facial expression recognition, reducing reliance on large labeled datasets by leveraging face recognition data for automatic annotation and achieving high accuracy.

Keywords:
deep joint learningefficient CNNexpression-guided deep facial clusteringfacial expression recognitionlimited labeled data

More Related Videos

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

632
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

448

Related Experiment Videos

Last Updated: Jul 18, 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

9.5K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

632
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

448

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Convolutional Neural Networks (CNNs) dominate facial expression recognition but require extensive labeled data and have high parameter counts.
  • Existing methods face challenges with limited training samples and computational overhead.

Purpose of the Study:

  • To develop an efficient CNN framework for facial expression recognition that minimizes the need for manually annotated data.
  • To improve the accuracy and efficiency of facial expression recognition systems, particularly for educational applications.

Main Methods:

  • A novel efficient CNN utilizing an affinity convolution module was developed for reduced computational cost.
  • An expression-guided deep clustering approach was employed to automatically label face recognition datasets for training.
  • The CNN was fine-tuned using a combined loss function and the newly annotated dataset.

Main Results:

  • The proposed method achieved 95.87% accuracy on a self-collected dataset for facial expression recognition in education.
  • The framework demonstrated superior performance compared to existing methods on multiple challenging datasets.
  • The affinity convolution module significantly reduced computational overhead.

Conclusions:

  • The developed deep joint learning framework effectively addresses the limitations of data scarcity and parameter inefficiency in facial expression recognition.
  • This approach offers a promising solution for real-world applications, such as educational monitoring, by enabling accurate and efficient expression analysis.