Robust precise eye location by adaboost and SVM techniques

  • Authors:

  • Xusheng Tang;Zongying Ou;Tieming Su;Haibo Sun;Pengfei Zhao

  • Affiliations:

  • Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology Dalian, Liaoning, China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology Dalian, Liaoning, China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology Dalian, Liaoning, China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology Dalian, Liaoning, China;Key Laboratory for Precision and Non-traditional Machining Technology of Ministry of Education, Dalian University of Technology Dalian, Liaoning, China

  • Venue:
  • ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2005

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Abstract

This paper presents a novel approach for eye detection using a hierarchy cascade classifier based on Adaboost statistical learning method combined with SVM (Support Vector Machines) post classifier. On the first stage a face detector is used to locate the face in the whole image. After finding the face, an eye detector is used to detect the possible eye candidates within the face areas. Finally, the precise eye positions are decided by the eye-pair SVM classifiers which using geometrical and relative position information of eye-pair and the face. Experimental results show that this method can effectively cope with various image conditions and achieve better location performance on diverse test sets than some newly proposed methods.