An Improvement of AdaBoost for Face-Detection with Motion and Color Information

An essential component of new advanced intelligent video surveillance systems is the possibility to perform non-cooperative people detection and identification. Nowadays Face Detection and Recognition are used in access control systems where the human being is willing to help the system, but the task is much more complex in other unconstrained situations. The paper refers some results of a preliminary study to investigate the most promising approach for face detection in noncooperative conditions with the main objective to reduce as much as possible the number of false alarms working on video-rate processing speed. The proposed solution has been developed around the AdaBoost approach, using the open-CV library, with an integration of motion and colour segmentation. The primary scope of the paper is to refer some experimental results to show the potential improvements in terms of reduction of false positives and a significant decrease of the execution time.