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

Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

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 of...
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

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...
Force Classification01:22

Force Classification

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,...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

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

Sort by
Same author

Spatially explicit temperature optima improve climate impact assessment of global crop productivity.

Nature communications·2026
Same author

Advancing the identification of toxic by-products in disinfected water by integrating effect-directed analysis and nontarget screening.

Journal of hazardous materials·2026
Same author

Single-Layer Narrow-Bandgap Co-Doped Ti<sub>2</sub>CO<sub>2</sub> MXene as a Multifunctional Cocatalyst for Scalable and Stable BiVO<sub>4</sub> Photoanodes.

ChemSusChem·2026
Same author

CmBt and CmBr synergistically regulate cucurbitacin B biosynthesis in melon.

Plant physiology·2026
Same author

Identification of a Prognostic Gene Signature Based on Lenvatinib Resistance in Hepatocellular Carcinoma with Functional Validation of the Key Gene CPB2.

Journal of hepatocellular carcinoma·2026
Same author

Nanoreactor-Mediated Covalent Anchoring Strategy for Robust, Fluorine-Free, and Multifunctional Superhydrophobic Textiles.

ACS applied materials & interfaces·2026
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jun 10, 2026

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

Facial expression recognition in JAFFE dataset based on Gaussian process classification.

Fei Cheng1, Jiangsheng Yu, Huilin Xiong

  • 1Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China. fcheng@bjtu.edu.cn

IEEE Transactions on Neural Networks
|August 24, 2010
PubMed
Summary
This summary is machine-generated.

Gaussian process (GP) classification models show high accuracy for facial expression recognition, even with small datasets. This novel method offers robust and significant performance improvements over existing classifiers.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 10, 2026

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

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

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Gaussian processes (GP) combine Bayesian methods and kernel techniques for small sample analysis.
  • Facial expression recognition is a challenging task in computer vision.

Purpose of the Study:

  • To propose and investigate a novel Gaussian process (GP) model for facial expression recognition.
  • To evaluate the performance of the GP model on the Japanese female facial expression dataset.

Main Methods:

  • Leave-one-out cross-validation was employed to assess classifier accuracy.
  • 10-fold cross-validation was repeated multiple times to evaluate robustness.
  • The GP model was applied without feature selection or extraction.

Main Results:

  • The GP classifier achieved 93.43% accuracy without feature engineering.
  • The GP classifier significantly outperformed common classifiers when tested on limited data.
  • Repeated cross-validation trials confirmed the method's robustness and promising recognition rates.

Conclusions:

  • The proposed Gaussian process model demonstrates a promising and robust approach for facial expression recognition.
  • This method is particularly effective for small sample analysis in computer vision tasks.