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

Learning Kernel Extended Dictionary for Face Recognition.

Ke-Kun Huang, Dao-Qing Dai, Chuan-Xian Ren

    IEEE Transactions on Neural Networks and Learning Systems
    |February 19, 2016
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Association Areas of the Cortex01:21

    Association Areas of the Cortex

    10.4K
    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,...
    10.4K
    Introduction to Learning01:18

    Introduction to Learning

    1.5K
    Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
    In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
    1.5K

    You might also read

    Related Articles

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

    Sort by
    Same author

    Open-Set Domain Adaptation via Target-Relaxed Optimal Transport.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
    Same author

    CGDock: Curvature-Aware Geometric Flow Framework for Protein-Ligand Docking.

    Journal of chemical information and modeling·2026
    Same author

    Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same author

    Partial Domain Adaptation via Importance Sampling-Based Shift Correction.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
    Same author

    When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights.

    IEEE transactions on pattern analysis and machine intelligence·2024
    Same author

    Geometric Understanding of Discriminability and Transferability for Visual Domain Adaptation.

    IEEE transactions on pattern analysis and machine intelligence·2024

    We introduce Kernel Extended Dictionary (KED) for face recognition, combining Kernel Discriminant Analysis (KDA) and Sparse Representation Classification (SRC). KED effectively handles occlusions and intraclass variations, achieving strong results even with limited data.

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Pattern Recognition

    Background:

    • Sparse Representation Classifier (SRC) excels at handling occlusions in face recognition.
    • Kernel Discriminant Analysis (KDA) effectively suppresses intraclass variations.
    • Combining SRC and KDA offers potential for improved face recognition performance.

    Purpose of the Study:

    • To propose Kernel Extended Dictionary (KED) for face recognition, integrating KDA and SRC.
    • To develop an efficient method for combining the strengths of KDA and SRC.
    • To enhance face recognition robustness against occlusions and intraclass variations.

    Main Methods:

    • Learning kernel principal components of occlusion variations to form an occlusion model.
    • Projecting the occlusion model using KDA to obtain the Kernel Extended Dictionary (KED).

    Related Experiment Videos

  • Utilizing structured SRC for classification with an appended dictionary and low feature dimension.
  • Extending KED to multikernel space for fusing diverse features.

    • Main Results:

    • KED achieves impressive results on nonoccluded face samples.
    • KED demonstrates robust performance in handling occlusions without overfitting.
    • Effective recognition is achieved even with a single gallery sample per subject.

    Conclusions:

    • KED offers an efficient and effective approach for face recognition by combining KDA and SRC.
    • The proposed method enhances robustness against occlusions and intraclass variations.
    • KED shows promise for real-world face recognition applications with limited training data.