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Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition.

Chengjun Liu1, Harry Wechsler

  • 1Dept. of Comput. Sci., New Jersey Inst. of Technol., Newark, NJ 07102, USA. liu@cs.njit.edu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 5, 2008
PubMed
Summary

A new Gabor-Fisher classifier (GFC) offers robust face recognition. This method achieves 100% accuracy using only 62 features, even with varying illumination and facial expressions.

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Area of Science:

  • Computer Vision
  • Biometrics
  • Machine Learning

Background:

  • Face recognition systems face challenges with variations in illumination and facial expressions.
  • Existing methods like Eigenfaces and Fisherfaces have limitations in handling these variations effectively.

Purpose of the Study:

  • To introduce a novel Gabor-Fisher classifier (GFC) for enhanced face recognition.
  • To develop a robust method for face recognition that is invariant to illumination and expression changes.
  • To evaluate the GFC method against other established face recognition techniques.

Main Methods:

  • An augmented Gabor feature vector is derived from Gabor wavelet representations of face images.
  • The enhanced Fisher linear discriminant model (EFM) is applied for dimensionality reduction, optimizing for compression and generalization.
  • A Gabor-Fisher classifier is developed for multi-class face recognition problems.

Main Results:

  • The novel GFC method achieved 100% accuracy in face recognition tests.
  • The GFC method demonstrated high performance using a reduced feature set of only 62 features.
  • Comparative studies showed the GFC method outperforming Eigenfaces, Fisherfaces, and Gabor wavelet methods.

Conclusions:

  • The Gabor-Fisher classifier (GFC) presents a highly accurate and efficient solution for face recognition.
  • The GFC method is robust to variations in illumination and facial expressions.
  • The GFC method offers a significant advancement in face recognition technology, particularly for real-world applications.