Abstract

Many researchers have reported that recognition accuracy improves when several images are continuously input into a recognition system. We call this recognition scheme a continuous observation- based scheme (CObS). The CObS is not only a useful and robust object recognition technique, it also offers a new direction in statistical pattern classification research. The main problem in statistical pattern recognition for the CObS is how to define the measure of similarity between two distributions. In this paper, we introduce some classifiers for use with continuous observations. We also experimentally demonstrate the effectiveness of continuous observation by comparing various classifiers.

Keywords

Face Recognition Object Recognition Facial Image Training Image Functional Space 
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 2002

Authors and Affiliations

  • Hitoshi Sakano
    • 1
  • Takashi Suenaga
    • 1
  1. 1.NTT Data Corp.TokyoJapan

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