PittPatt Face Detection and Tracking for the CLEAR 2007 Evaluation

  • Michael C. Nechyba
  • Louis Brandy
  • Henry Schneiderman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4625)


This paper describes Pittsburgh Pattern Recognition’s participation in the face detection and tracking tasks for the CLEAR 2007 evaluation. Since CLEAR 2006, we have made substantial progress in optimizing our algorithms for speed, achieving better than real-time processing performance for a speed-up of more than 500× over the past two years. At the same time, we have maintained the high level of accuracy of our algorithm. In this paper, we first give a system overview, briefly explaining the three main stages of processing: (1) frame-based face detection; (2) motion-based tracking; and (3) track filtering. Second, we report our results, both in terms of accuracy and speed, over the CHIL and VACE test data sets. Finally, we offer some analysis on both speed and accuracy performance


False Alarm Video Frame Face Detection Face Track VACE Data 
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 2008

Authors and Affiliations

  • Michael C. Nechyba
    • 1
  • Louis Brandy
    • 1
  • Henry Schneiderman
    • 1
  1. 1.Pittsburgh Pattern RecognitionPittsburghUSA

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