NOVEL CASCADE KERNEL CONFIGURATIONS OF HAAR CLASSIFIERS IN BOOSTING BASED MACHINE LEARNING FOR IMPROVED 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. Machine learning of haar features is an important task 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 finding the novel cascade kernel configurations in boosting based machine learning of haar features for the task of face detection. Based on the studying of parameters during training process, a number of cascade kernel configurations have been investigated and compared for their performance on benchmark datasets