Face recognition using Evolutionary Pursuit

  • Chengjun Liu
  • Harry Wechsler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)


This paper describes a novel and adaptive dictionary method for face recognition using genetic algorithms (GAs) in determining the optimal basis for encoding human faces. In analogy to pursuit methods, our novel method is called Evolutionary Pursuit (EP), and it allows for different types of (non-orthogonal) bases. EP processes face images in a lower dimensional whitened PCA subspace. Directed but random rotations of the basis vectors in this subspace are searched by GAs where evolution is driven by a fitness function defined in terms of performance accuracy and class separation (scatter index). Accuracy indicates the extent to which learning has been successful so far, while the scatter index gives an indication of the expected fitness on future trials. As a result, our approach improves the face recognition performance compared to PCA, and shows better generalization abilities than the Fisher Linear Discriminant (FLD) based methods.


Basis Vector Face Recognition Recognition Rate Optimal Basis Projection Pursuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Chengjun Liu
    • 1
  • Harry Wechsler
    • 1
  1. 1.Department of Computer ScienceGeorge Mason UniversityFairfaxUSA

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