Improved Training Algorithms to Reduced Set Vector Machine and Adaboost Cascade Classifier for Face Detection

In this paper we present improved training algorithms to two newly developed classifiers, reduced set vector machines and Adaboost cascade classifier applied in face detection, which are all based on learning from data. Support vector machine (SVM) has been proved to be a powerful tool for solving practical pattern recognition problems based on learning from data. Due to large number of support vectors learnt from huge amount of training data the SVM becomes too computational intensive to many critical problems. Reduced set vector machine (RVM) is a faster approximation of SVM, but calculate a RVM is very difficult. Cascade classifier using Adaboost is a newly proposed method, which is much faster than the SVM and MLP methods and very competitive in performance to the existing systems, but the training is not easy due to feature number option. Our improved training algorithms make training become easier and more reliable and applicable in practice.