TRAINING ANALYSIS OF HAAR-CLASSIFIERS FOR FACE DETECTION

Haar features have been used in most of the works in literature as key components in the task of object as well as face detection. Training process of Haar features is an important step in the development of overall face detection system. A number of features can be observed in the training process of Haar features. In this work, we have done investigation in the training parameters during AdaBoost based machine learning of Haar features. Based on the studying of parameters during training process, efficient learners can be selected from a large pool of available features which are further cascaded to make the strong classifiers. Modification in face detection process by application of scaling of detector window by changing the base size of training samples and efficient detection technique has been proposed.