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Discriminative common vectors for face recognition.

Hakan Cevikalp1, Marian Neamtu, Mitch Wilkes

  • 1Department of Electrical Engineering and Computer Science, Vanderbilt University, Box 131, Station B, Nashville, TN 37235, USA. hakan.cevikalp@vanderbilt.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 5, 2005
PubMed
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A new Discriminative Common Vector method effectively addresses the small sample size problem in face recognition. This approach improves accuracy and efficiency compared to traditional methods.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Face recognition faces challenges with high-dimensional data and limited training samples.
  • The small sample size problem renders traditional Linear Discriminant Analysis (LDA) singular and inapplicable.
  • Existing methods struggle with recognition accuracy and efficiency in low-sample scenarios.

Purpose of the Study:

  • To introduce a novel face recognition method, the Discriminative Common Vector (DCV) method.
  • To adapt Fisher's Linear Discriminant Analysis for the small sample size problem in face recognition.
  • To develop algorithms for extracting discriminative common vectors for robust classification.

Main Methods:

  • Proposed the Discriminative Common Vector (DCV) method, a variation of Fisher's LDA.

Related Experiment Videos

  • Developed two algorithms for DCV extraction: one using within-class scatter, another using subspace methods and Gram-Schmidt orthogonalization.
  • Utilized extracted DCVs for classifying new faces.

    • Main Results:

    • The DCV method demonstrated superior recognition accuracy compared to other existing methods.
    • Achieved enhanced efficiency and numerical stability in face recognition tasks.
    • The method provides an optimal solution by maximizing a modified Fisher's Linear Discriminant criterion.

    Conclusions:

    • The Discriminative Common Vector method is a highly effective solution for face recognition under small sample size conditions.
    • DCV offers significant improvements in accuracy, efficiency, and numerical stability.
    • This approach advances the field of pattern recognition for challenging datasets.