A new efficient SVM and its application to real-time accurate eye localization

Shuo Chen, Chengjun Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

For complicated classification problems, the standard Support Vector Machine (SVM) is likely to be complex and thus the classification efficiency is low. In this paper, we propose a new efficient SVM (eSVM), which is based on the idea of minimizing the margin of misclassified samples. Compared with the conventional SVM, the eSVM is defined on fewer support vectors and thus can achieve much faster classification speed and comparable or even higher classification accuracy. We then present a real-time accurate eye localization system using the eSVM together with color information and 2D Haar wavelet features. Experiments on some public data sets show that (i) the eSVM significantly improves the efficiency of the standard SVM without sacrificing its accuracy and (ii) the eye localization system has real-time speed and higher detection accuracy than some state-of-the-art approaches.

Original languageEnglish (US)
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages2520-2527
Number of pages8
DOIs
StatePublished - Oct 24 2011
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: Jul 31 2011Aug 5 2011

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CountryUnited States
CitySan Jose, CA
Period7/31/118/5/11

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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