TY - GEN
T1 - A new efficient SVM and its application to real-time accurate eye localization
AU - Chen, Shuo
AU - Liu, Chengjun
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=80054733300&partnerID=8YFLogxK
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U2 - 10.1109/IJCNN.2011.6033547
DO - 10.1109/IJCNN.2011.6033547
M3 - Conference contribution
AN - SCOPUS:80054733300
SN - 9781457710865
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 2520
EP - 2527
BT - 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
T2 - 2011 International Joint Conference on Neural Network, IJCNN 2011
Y2 - 31 July 2011 through 5 August 2011
ER -