Image Context-Driven Eye Location Using the Hybrid Network of k-Means and RBF

  • Eun Jin Koh
  • Phill Kyu Rhee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


In this paper, we present a novel eye location approach based on image context analysis. It is robust from the image variations such as illumination, glasses frame, and eyebrows. Image context of an image is any observable relevant attributes with other images. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed eye location employs context-driven adaptive Bayesian framework to relive the effect due to uneven face images. The appearance of eye patterns is represented by Haar wavelet. It also employs a merging and arbitration strategy in order to manage the variations in illumination and geometrical characteristics of ambient eye regions due to glasses frames, eye brows, and so on. The located eye candidates are merged or eliminated, and adaptive arbitration strategy is used based on a minimizing energy function by probabilistic forces and image forces. The adaptation is carried out by the analysis of image context. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously proposed methods.


Face Image False Detection Bayesian Classifier Hybrid Network Probabilistic Force 
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

  • Eun Jin Koh
    • 1
  • Phill Kyu Rhee
    • 1
  1. 1.Department of computer science & EngineeringInha University, Biometric Engineering Research CenterIncheonKorea

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