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ACCV
2007
Springer

A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost

14 years 5 months ago
A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost
AdaBoost is a practical method of real-time face detection, but abides by a crucial problem of overfitting for the big number of features used in a trained classifier due to the weak discriminative abilities of these features. This paper proposes a theoretical approach to construct highly discriminative features, which is named composed features, from Haar-like features. Both of the composed and Haar-like features are employed to train a multi-view face detector. The primary experiments show promising results in reducing the number of features used in a classifier, which leads to the increase of the generalization ability of the classifier.
Yuxin Jin, Linmi Tao, Guangyou Xu, Yuxin Peng
Added 06 Jun 2010
Updated 06 Jun 2010
Type Conference
Year 2007
Where ACCV
Authors Yuxin Jin, Linmi Tao, Guangyou Xu, Yuxin Peng
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