a systematic training procedure for viola-jones face detector in heterogeneous computing architecture

The face detection has become one of the most important topics in machine learning and computer vision in the last few decades. many papers have been published utilizing various methods for face detection. one of the most popular face detectors used in many practical applications with heterogeneous computing architecture 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, e.g., 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 trainset for training a cascade. in this paper, a trainset selection method based on histograms generated from adaboost and in addition, simple ways to select few features in beginning cascades are proposed. the training procedure is then compared to a baseline training presented in the previous studies.