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Related Experiment Videos

Face recognition using recursive Fisher linear discriminant.

C Xiang1, X A Fan, T H Lee

  • 1Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576. elexc@nus.edu.sg

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|August 12, 2006
PubMed
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A new recursive algorithm enhances Fisher linear discriminant (FLD) for feature extraction, overcoming FLD's limitations. This method significantly improves performance in pattern classification, particularly for face recognition tasks.

Area of Science:

  • Computer Science
  • Pattern Recognition
  • Machine Learning

Background:

  • Fisher linear discriminant (FLD) is an efficient feature extraction method for pattern classification.
  • Traditional FLD has limitations due to constraints on the number of extractable features.
  • Principal Component Analysis (PCA) is a traditional alternative for feature extraction.

Purpose of the Study:

  • To propose a novel recursive algorithm for calculating discriminant features.
  • To overcome the feature number limitations of traditional Fisher linear discriminant.
  • To enhance feature extraction for pattern classification problems.

Main Methods:

  • A recursive procedure for calculating discriminant features was developed.
  • The new algorithm maintains the core FLD principle of class-separating projections.

Related Experiment Videos

  • The number of derived features is independent of the number of classes.

    • Main Results:

    • The new algorithm demonstrated significant performance improvements.
    • Experiments were conducted on the face recognition problem using the Yale database.
    • The recursive feature extraction scheme outperformed traditional approaches.

    Conclusions:

    • The proposed recursive algorithm effectively addresses FLD's limitations.
    • This new method offers a more flexible and powerful approach to feature extraction.
    • The technique shows significant promise for complex pattern recognition tasks like face recognition.