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.

• Type: Conference
• Year: 2007
• Where: ACCV
• Authors: Yuxin Jin, Linmi Tao, Guangyou Xu, Yuxin Peng
• Keywords: ACCV 2007, Computer Vision, Discriminative Features, Haar-like Features, Weak Discriminative Abilities