Face recognition using evolutionary pursuit

Chengjun Liu, Harry Wechsler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 1998 - 5th European Conference on Computer Vision, Proceedings
EditorsBernd Neumann, Hans Burkhardt
PublisherSpringer Verlag
Number of pages17
ISBN (Print)3540646132, 9783540646136
StatePublished - 1998
Externally publishedYes
Event5th European Conference on Computer Vision, ECCV 1998 - Freiburg, Germany
Duration: Jun 2 1998Jun 6 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other5th European Conference on Computer Vision, ECCV 1998

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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