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 Videos

Individual Kernel Tensor-subspaces for robust face recognition: a computationally efficient tensor framework without

Sung Won Park1, Marios Savvides

  • 1Carnegie Mellon University, Pittsburgh, PA 15213, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 12, 2007
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

Measuring the potential risk of re-identification of imaging research participants from open-source automated face recognition software.

NeuroImage·2025
Same author

Validation of combo ichroma as a reliable concentration-based alternative for AST and ALT measurement in liver disease monitoring.

Methods (San Diego, Calif.)·2025
Same author

A risk prediction model for hepatocellular carcinoma in non-alcoholic fatty liver disease without cirrhosis: HCC prediction for non-cirrhotic NAFLD.

Liver international : official journal of the International Association for the Study of the Liver·2023
Same author

Predicting prognosis through the discovery of specific biomarkers according to colorectal cancer lymph node metastasis.

American journal of cancer research·2023
Same author

Increase of Prevalence of Obesity and Metabolic Syndrome in Children and Adolescents in Korea during the COVID-19 Pandemic: A Cross-Sectional Study Using the KNHANES.

Children (Basel, Switzerland)·2023
Same author

A face-off of MRI research sequences by their need for de-facing.

NeuroImage·2023
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

This study introduces Individual Kernel TensorFaces, a novel tensor approach for face recognition that overcomes limitations of traditional methods. It enhances computational efficiency and accuracy without needing to factorize test image parameters.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Facial image appearance varies significantly due to pose, lighting, and expression.
  • Tensors offer a powerful framework for modeling these multilinear variations in facial data.
  • Traditional tensor-based face recognition methods face challenges with parameter factorization for new images.

Purpose of the Study:

  • To propose a novel tensor approach for face recognition that addresses the limitations of existing tensor factorization methods.
  • To enhance the computational efficiency and accuracy of face recognition systems.
  • To develop a method that does not require estimating factors of new test images.

Main Methods:

  • Developed a novel tensor approach using an individual-modeling method and nonlinear kernel mappings.

Related Experiment Videos

  • Formulated the problem of solving for unknown factors as a least squares problem with quadratic equality constraints.
  • Employed numerical optimization techniques to solve the constrained least squares problem.

    • Main Results:

    • The proposed Individual Kernel TensorFaces method demonstrates improved computational and memory efficiency compared to TensorFaces.
    • Nonlinear kernel mappings provide higher accuracy in face recognition than linear mappings.
    • The method achieves better discrimination power for classification and reliable face recognition results on benchmark datasets.

    Conclusions:

    • Individual Kernel TensorFaces offers a computationally efficient and analytically simpler approach to face recognition.
    • The method eliminates the need for problematic tensor factorization and estimation of test image factors.
    • This approach provides a robust and accurate face recognition system, even with variations or missing factors in test images.