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Related Experiment Videos

Total variation models for variable lighting face recognition.

Terrence Chen1, Wotao Yin, Xiang Sean Zhou

  • 1University of Illinois at Urbana Champaign, 405 N. Mathews Ave., Urbana, IL 61801, USA. tchen5@ifp.uiuc.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 26, 2006
PubMed
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The new logarithmic total variation (LTV) model enables robust face recognition despite unknown lighting variations. This illumination-invariant method achieves high accuracy without needing training or lighting assumptions.

Area of Science:

  • Computer Vision
  • Biometrics
  • Image Processing

Background:

  • Varying illumination poses a significant challenge in face recognition systems.
  • Accurate face recognition requires handling diverse and often unpredictable lighting conditions.

Purpose of the Study:

  • To introduce the logarithmic total variation (LTV) model for illumination-invariant face recognition.
  • To develop a method that does not require prior knowledge of lighting parameters or training data.

Main Methods:

  • The LTV model factorizes face images to extract an illumination-invariant facial structure.
  • The model is inspired by the SQI model, offering improved edge preservation and simpler parameter selection.

Main Results:

Related Experiment Videos

  • The LTV model demonstrates superior edge-preserving capabilities compared to the SQI model.
  • High face recognition rates were achieved on the Yale and CMU PIE face databases.
  • Excellent performance was observed on a large outdoor face dataset with 765 subjects.

    • Conclusions:

    • The LTV model provides an effective solution for face recognition under challenging, unconstrained illumination.
    • The model's ability to work without lighting assumptions or training makes it highly practical.