Paper: Memory-based Face Recognition

[sbaluja@lycos.com]

Paper:
   High-Performance Memory-based Face Recognition
   for Visitor Identification

  JPRC-Technical Report-1999-01

Authors:
   Terence Sim, Rahul Sukthankar, Matthew D. Mullin & Shumeet Baluja

Available from:
   http://www.cs.cmu.edu/~baluja/techreps.html
   &
   http://www.cs.cmu.edu/~rahuls/pub/


Abstract:
    We show that a simple, memory-based technique for view-based face
    recognition, motivated by the real-world task of visitor
    identification, can outperform more sophisticated algorithms that
    use Principal Components Analysis (PCA) and neural networks.
    This technique is closely related to correlation templates;
    however, we show that the use of novel similarity measures
    greatly improves performance.  We also show that augmenting the
    memory base with additional, synthetic face images results in
    further improvements in performance.  Results of extensive
    empirical testing on two standard face recognition datasets are
    presented, and direct comparisons with published work show that
    our algorithm achieves comparable (or superior) results.  This
    paper further demonstrates that our algorithm has desirable
    asymptotic computational and storage behavior, and is ideal for
    incremental training.  Our system is incorporated into an
    automated visitor identification system that has been operating
    successfully in an outdoor environment for several months.

Contact:
  tsim at jprc.com, rahuls at jprc.com, mdm at jprc.com, sbaluja at lycos.com

Comments and Questions are welcome!