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

Association Areas of the Cortex01:21

Association Areas of the Cortex

10.2K
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,...
10.2K
Parallel Processing01:20

Parallel Processing

943
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
943

You might also read

Related Articles

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

Sort by
Same author

System Performance Analysis for an Energy Harvesting IoT System Using a DF/AF UAV-Enabled Relay with Downlink NOMA under Nakagami-<i>m</i> Fading.

Sensors (Basel, Switzerland)·2021
Same author

Integrating new data balancing technique with committee networks for imbalanced data: GRSOM approach.

Cognitive neurodynamics·2015
Same author

Local matrix learning in clustering and applications for manifold visualization.

Neural networks : the official journal of the International Neural Network Society·2010
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

9.2K

PEM-PCA: a parallel expectation-maximization PCA face recognition architecture.

Kanokmon Rujirakul1, Chakchai So-In1, Banchar Arnonkijpanich2

  • 1Applied Network Technology (ANT) Laboratory, Department of Computer Science, Faculty of Science, Khon Kaen University, Khon Kaen, Thailand.

Thescientificworldjournal
|June 24, 2014
PubMed
Summary

This study introduces a faster face recognition method using an Expectation-Maximization algorithm and parallel processing, significantly reducing computational complexity for efficient, high-speed systems.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Related Experiment Videos

Last Updated: Apr 27, 2026

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

9.2K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Principal Component Analysis (PCA) is a traditional feature extraction technique in face recognition.
  • PCA offers high accuracy but suffers from high computational complexity due to covariance matrix and eigenvalue decomposition, especially with large datasets.

Purpose of the Study:

  • To present an alternative approach to PCA for face recognition with reduced computational complexity.
  • To improve computational time in face recognition systems through parallelization.

Main Methods:

  • Utilized an Expectation-Maximization (EM) algorithm to reduce matrix manipulation complexity.
  • Developed a novel parallel architecture for feature extraction and classification stages.
  • Combined preprocessing, feature extraction, and classification into a Parallel Expectation-Maximization PCA architecture.

Main Results:

  • The proposed EM-based approach significantly reduced computational complexity compared to traditional PCA.
  • The Parallel EM-PCA architecture achieved a speed-up of over nine times compared to PCA and three times compared to Parallel PCA.
  • Recognition precision remained insignificantly different from traditional PCA methods.

Conclusions:

  • The Parallel Expectation-Maximization PCA architecture offers a highly efficient and fast face recognition solution.
  • This approach effectively balances computational complexity reduction with high recognition accuracy.
  • The method is suitable for developing high-speed face recognition systems.