Feature Extraction and Evolution Based Pattern Recognition

  • Mi Young Nam
  • Phill Kyu Rhee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


This paper proposes a novel method of classifier selection for efficient object recognition based on evolutionary computation and data context knowledge called Evolvable Classifier Selection. The proposed method tries to distinguish the data characteristics of input image (data contexts) and selects a classifier system accordingly using the genetic algorithm. It stores its experiences in terms of the data context category and the artificial chromosome so that the context knowledge can be accumulated and used later. The proposed method operates in two modes: the evolution mode and the action mode. It can improve its performance incrementally using GA in the evolution mode. Once sufficient context knowledge is accumulated, the method can operate in real-time. The proposed method has been evaluated in the area of face recognition. Data context-awareness, modeling and identification of input data as data context categories, is carried out using SOM.


Face Recognition Context Model Data Context Context Knowledge Fiducial Point 
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 2006

Authors and Affiliations

  • Mi Young Nam
    • 1
  • Phill Kyu Rhee
    • 1
  1. 1.Dept. of Computer Science & EngineeringInha UniversityIncheonSouth Korea

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