Application of Haar-like Features in Three AdaBoost Algorithms for Face Detection

Abstract

In this paper we introduce a proposed cascade classifier system consists of five strong classifiers (stages), and empirically analysis three types of Adaboost algorithms (Real, Modest, and Gentle) which it were used for boosting the strong classifiers. Five prototypes of haar-like features (two-horizontal rectangles, two-vertical rectangles, three-horizontal rectangles, three-vertical rectangles, and four-rectangles) are used for each strong classifier (stage) of cascade classifier. Two-horizontal, two-vertical rectangles features and Modest-Adaboost Algorithm are applied for the 1st and 2nd strong classifier respectively, three-horizontal, vertical-rectangles features and Real-Adaboost Algorithm are applied for 3rd and 4th strong classifier respectively, and the last, four-rectangles and Real-Adaboost Algorithm are applied for the 5th strong classifier of cascade classifier. The implementation shows that the best use of Adaboost algorithm is: the Modest-Adaboost algorithm for both 1st and 2nd strong classifier, and the Real-Adaboost algorithm for 3rd,4th and 5th strong classifier.