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Merging and Arbitration Strategy for Robust Eye Location

  • Eun Jin Koh
  • Phill Kyu Rhee
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)

Abstract

The difficulties of eye location are mainly caused by the variations of intrinsic eye pattern characteristics from people to people, scale, pose, glasses frame, illumination, etc. To prevail from these problems, this paper addresses a novel and precise robust eye location method. It employs appearance based Bayesian framework to relive the effect of uneven illumination. The appearance of eye patterns is represented by 2D Haar wavelet. It also employs a sophisticated merging and arbitration strategy in order to manage the variations in geometrical characteristics of ambient eye regions due to glasses frames, eye brows, and so on. The located eye candidates are merged or eliminated according to the merging rule. If the merged regions are more than one, we apply the arbitration strategy. The arbitration strategy is based on a minimizing energy function by probabilistic forces and image forces that pull it toward eyes. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously proposed methods.

Keywords

Mahalanobis Distance Bayesian Classifier Probabilistic Force Merging Strategy Uneven Illumination 
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 & Engineering Inha UniversityBiometric Engineering Research Center Young-Hyun DongIncheonKorea

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