Seyyed Majid valiollahzadeh - Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Abolghasem Sayadiyan - Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran
Mohammad Nazari - Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

Boosting is a general methodfor improving the accuracy of any given learning algorithm. In this pctper we employ combination of Adaboost with Support Vector Machine (SVM) as component classffiers to be used in Face Detection Task Proposed combination outperforms in generalization in comparison with SVM on imbalanced classification problem. The proposed here method is compared, in terms of classification accuracy, to other commonly used Adaboost methods, such as Decision Trees and Neural Networks, on CMU+MIT face database. Results indicate that the performance ofthe proposed method is overall superior to previotu adaboost approaches.

Face Detection, Cascaded Classifiers, Adaboost, Support Vector Machine (SVM)