A theoretical approach to construct highly discriminative features with application in AdaBoost

Authors:
Yuxin Jin;Linmi Tao;Guangyou Xu;Yuxin Peng
Affiliations:
Computer Science and Technology Department, Tsinghua University, Beijing, China;Computer Science and Technology Department, Tsinghua University, Beijing, China;Computer Science and Technology Department, Tsinghua University, Beijing, China;Computer Science and Technology Department, Tsinghua University, Beijing, China
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

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Abstract

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.