An Efficient Training Procedure for Viola-Jones Face Detector

During the past decades, face detection has attracted significant attention. Many papers have been published utilizing various methods for face detection. One of the most popular face detectors used in many practical applications is Viola-Jones method. Despite of being a real-time and robust face detector, it suffers from not well explained parts at the training procedure. Such as, "not clear how to select few features in the first cascades" or "not clear how many samples are needed and how to gather a good train set for training a cascade". In this paper, a train set selection method based on histograms generated from AdaBoost and also, a simple method to select few features in beginning cascades are proposed. The training procedure is then compared to a baseline training presented in the previous works.