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 Experiment Video

Updated: Jul 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees.

M Zhang1, J Fulcher

  • 1Dept. of Comput. and Inf. Syst, Univ. of Western Sydney, NSW.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Search for Light Pseudoscalar Bosons, Pair-Produced in Higgs Boson Decays in the Four-Electron Final State in Proton-Proton Collisions at sqrt[s]=13  TeV.

Physical review letters·2026
Same author

Observation of Suppressed Charged-Particle Production in Ultrarelativistic Oxygen-Oxygen Collisions.

Physical review letters·2026
Same author

Observation of tWZ Production at the CMS Experiment.

Physical review letters·2026
Same author

First Exclusive Reconstruction of the B^{*+}, B^{*0}, and B_{s}^{*0} Mesons and Precise Measurement of Their Masses.

Physical review letters·2026
Same author

Simultaneous Probe of the Charm and Bottom Quark Yukawa Couplings Using tt[over ¯]H Events.

Physical review letters·2026
Same author

Measurement of WWZ and ZH Production Cross Sections at sqrt[s]=13 and 13.6 TeV.

Physical review letters·2025
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

A new artificial neural network group-based adaptive tolerance (GAT) tree model improves face recognition. This advanced model is suitable for airport security systems, offering better performance than traditional methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Simple artificial neural network models struggle with complex tasks like face recognition.
  • Existing methods lack robustness for real-world applications such as airport security.

Purpose of the Study:

  • Introduce the artificial neural network group-based adaptive tolerance (GAT) tree model.
  • Enable translation-invariant face recognition for enhanced security systems.

Main Methods:

  • Developed a two-stage, divide-and-conquer GAT tree approach.
  • Stage one: identifies general facial properties (e.g., glasses, beard).
  • Stage two: performs individual identification.

Main Results:

Related Experiment Videos

Last Updated: Jul 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

  • Demonstrated effective face perception classification.
  • Achieved accurate detection of front faces with varying features.
  • GAT trees outperformed conventional neural network trees in laboratory tests.

    • Conclusions:

    • The GAT tree model provides significant improvements for face recognition tasks.
    • This model is well-suited for translation-invariant recognition in security applications.