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Robust Face Recognition in the Presence of Clutter

  • A.N. Rajagopalan
  • Rama Chellappa
  • Nathan Koterba
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)

Abstract

We propose a new method within the framework of principal component analysis to robustly recognize faces in the presence of clutter. The traditional eigenface recognition method performs poorly when confronted with the more general task of recognizing faces appearing against a background. It misses faces completely or throws up many false alarms. We argue in favor of learning the distribution of background patterns and show how this can be done for a given test image. An eigenbackground space is constructed and this space in conjunction with the eigenface space is used to impart robustness in the presence of background. A suitable classifier is derived to distinguish non-face patterns from faces. When tested on real images, the performance of the proposed method is found to be quite good.

Keywords

False Alarm Test Image Reconstruction Error Face Detection Background Class 
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 2003

Authors and Affiliations

  • A.N. Rajagopalan
    • 1
  • Rama Chellappa
    • 2
  • Nathan Koterba
    • 2
  1. 1.Indian Institute of TechnologyMadrasIndia
  2. 2.Center for Automation ResearchUniversity of MarylandCollege ParkUSA

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