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A shape- and texture-based enhanced Fisher classifier for face recognition.

C Liu1, H Wechsler

  • 1Department of Mathematics and Computer Science, University of Missouri, St. Louis, MO 63121, USA. cliu@cs.umsl.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 6, 2008
PubMed
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A novel face recognition method, the enhanced Fisher classifier (EFC), integrates shape and texture features for superior accuracy. This approach achieves 98.5% recognition accuracy using just 25 features, outperforming existing methods.

Area of Science:

  • Computer Science
  • Biometrics
  • Pattern Recognition

Background:

  • Face recognition systems are crucial for security and identification.
  • Existing methods often struggle with variations in illumination and facial expressions.
  • Integrating diverse feature types can enhance recognition robustness.

Purpose of the Study:

  • To introduce a new face coding and recognition method, the enhanced Fisher classifier (EFC).
  • To evaluate the effectiveness of integrated shape and texture features for face recognition.
  • To compare the performance of EFC against established face recognition techniques.

Main Methods:

  • Developed the enhanced Fisher classifier (EFC) utilizing the enhanced Fisher linear discriminant model (EFM).
  • Integrated shape (geometry) and texture (normalized image) features.

Related Experiment Videos

  • Reduced feature dimensionality using principal component analysis constrained by EFM.
  • Combined reduced features via normalization for EFM processing.

    • Main Results:

    • Integrated shape and texture features provided the most discriminating information.
    • The EFC method demonstrated superior performance compared to eigenfaces and Mahalanobis distance classifiers.
    • Achieved 98.5% recognition accuracy with only 25 features.

    Conclusions:

    • The EFC method offers a highly accurate and efficient approach to face recognition.
    • Combining shape and texture features significantly improves recognition performance.
    • The EFC method shows strong generalization capabilities under varying conditions.