Illumination Invariant Face Image Representation Using Quaternions

  • Dayron Rizo-Rodríguez
  • Heydi Méndez-Vázquez
  • Edel García-Reyes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6419)

Abstract

Variations in illumination is a well-known affecting factor of face recognition system performance. Feature extraction is one of the principal steps on a face recognition framework, where it is possible to alleviate the illumination effects on face images. The aim of this work is to study the illumination invariant properties of a hypercomplex image representation. A quaternion description from the image is built using second order derivatives decomposition. This representation is transformed to quaternion frequency domain in order to analyze its illumination invariant and discriminative properties, which are compared against the ones of the complex frequency domain representation obtained by using first order derivative decomposition. The hypercomplex quaternion representation was found to be more discriminative than the complex one, when comparing on face recognition with images under varying lighting conditions.

Keywords

Face Recognition Face Image False Acceptance Rate False Rejection Rate Discriminative Property 
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 2010

Authors and Affiliations

  • Dayron Rizo-Rodríguez
    • 1
  • Heydi Méndez-Vázquez
    • 1
  • Edel García-Reyes
    • 1
  1. 1.Advanced Technologies Application CenterPlayaCuba

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