Jove
Visualize
Contact Us

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

7.7K
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,...
7.7K
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

3.9K
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...
3.9K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

399
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...
399
Prosopagnosia01:24

Prosopagnosia

538
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...
538

You might also read

Related Articles

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

Sort by
Same author

Dietary diversity status and influencing factors among children aged 3-7 in China.

Frontiers in nutrition·2026
Same author

To assure aviation safety: the pilot fatigue detection based on short-term multimodal physiological signals.

Frontiers in human neuroscience·2026
Same author

Carbon footprint dataset of concrete based on field surveys at commercial mixing plants in Shandong, China.

Scientific data·2026
Same author

Dynamic Simulation and Causal Mechanisms of Eutrophication in Irregular Shallow Urban Lakes Based on Numerical Models.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Artificial Intelligence-Driven Food Safety: Decoding Gut Microbiota-Mediated Health Effects of Non-Microbial Contaminants.

Foods (Basel, Switzerland)·2026
Same author

Amide Hydrochloride Molecules Enable Energy-Efficient Near-Infrared Perovskite LEDs.

ACS nano·2025
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
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
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: Nov 19, 2025

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

15.7K

Facial Expression Recognition with LBP and ORB Features.

Ben Niu1, Zhenxing Gao2, Bingbing Guo3

  • 1School of Electronic and Information Engineering, Jinling Institute of Technology, Nanjing 211169, China.

Computational Intelligence and Neuroscience
|January 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel facial expression recognition algorithm using Oriented FAST and Rotated BRIEF (ORB) and Local Binary Patterns (LBP) features. The method achieves accurate emotion recognition on low-specification hardware, outperforming deep neural networks.

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

9.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Related Experiment Videos

Last Updated: Nov 19, 2025

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

15.7K
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

9.7K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Facial expression recognition is crucial for human-computer interaction.
  • Deep neural networks (DNNs) dominate this field but require high hardware specifications.
  • Existing methods face limitations in real-world applications with low-resource hardware.

Purpose of the Study:

  • To propose an effective facial expression recognition algorithm for low-specification hardware.
  • To develop a method that avoids the high computational demands of deep neural networks.
  • To enhance the accuracy and efficiency of emotion recognition in human-computer interaction.

Main Methods:

  • A novel algorithm combining Oriented FAST and Rotated BRIEF (ORB) and Local Binary Patterns (LBP) features for facial expression analysis.
  • Face detection is employed first to isolate facial regions for feature extraction.
  • Innovative region division in ORB feature extraction and classification using Support Vector Machine (SVM).

Main Results:

  • The proposed method demonstrates effectiveness and accuracy across multiple challenging facial expression databases (CK+, JAFFE, MMI).
  • Features extracted are invariant to scale, grayscale, and rotation changes.
  • Achieved accurate recognition of seven distinct emotion states.

Conclusions:

  • The ORB and LBP feature combination offers a viable alternative to DNNs for facial expression recognition on resource-constrained systems.
  • The proposed algorithm provides a computationally efficient and accurate solution for real-world human-computer interaction.
  • This framework significantly advances the applicability of facial expression recognition in diverse environments.