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

Face detection using spectral histograms and SVMs.

Christopher A Waring1, Xiuwen Liu

  • 1Department of Computer Science, The Florida State University, Tallahassee, FL 32306, USA. chwaring@cs.fsu.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|June 24, 2005
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

Comparative efficacy of levosimendan and dobutamine in sepsis-related cardiac impairment: a meta-analysis.

European journal of clinical pharmacology·2026
Same author

Endoscopic purse-string suture technique for postoperative drain-related leakage management in gastrointestinal surgery.

BMC surgery·2025
Same author

Infrared Ship Detection in Complex Nearshore Scenes Based on Improved YOLOv5s.

Sensors (Basel, Switzerland)·2025
Same author

Self-Interested Coalitional Crowdsensing for Multi-Agent Interactive Environment Monitoring.

Sensors (Basel, Switzerland)·2024
Same author

Targeting DDX11 promotes PARP inhibitor sensitivity in hepatocellular carcinoma by attenuating BRCA2-RAD51 mediated homologous recombination.

Oncogene·2023
Same author

Unveiling immune-metabolic interaction in Atherosclerosis via Comprehensive Landscape of Hub Genes and Immune Microenvironment.

The heart surgery forum·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 a novel face detection method using spectral histograms and support vector machines (SVMs). This approach achieves superior performance in identifying faces across various conditions.

Area of Science:

  • Computer Vision
  • Machine Learning
  • Pattern Recognition

Background:

  • Accurate face detection is crucial for numerous applications.
  • Existing methods often struggle with variations in lighting and image conditions.
  • Developing robust feature representations is key to improving face detection accuracy.

Purpose of the Study:

  • To develop a novel face detection method utilizing spectral histograms and support vector machines (SVMs).
  • To demonstrate the effectiveness of spectral histograms as a robust feature representation for faces.
  • To achieve high performance in face detection across diverse and challenging image conditions.

Main Methods:

  • Representing image windows using spectral histograms, which are feature vectors derived from filtered image histograms.

Related Experiment Videos

  • Employing statistical sampling to validate the grouping property of spectral histograms for face images.
  • Training a support vector machine (SVM) classifier on a large dataset of face (4500) and non-face (8000) images.
  • Implementing an illumination-correction algorithm to enhance robustness under varying conditions.

    • Main Results:

    • The spectral histogram representation effectively groups face images, unlike many conventional methods.
    • The trained SVM classifier provides a robust function for distinguishing face and non-face patterns.
    • The system demonstrates reliable discrimination of faces under diverse conditions due to illumination correction.
    • The proposed method achieved state-of-the-art performance on two standard face detection datasets.

    Conclusions:

    • The spectral histogram representation offers desirable properties for face detection.
    • Support vector machines provide excellent generalization capabilities for this task.
    • The combined approach yields a highly effective and robust face detection system.
    • Further improvements in computational efficiency and performance are feasible.