Online Boosting OC for Face Recognition in Continuous Video Stream

In this paper, we present a novel online face recognition approach for video stream called online boosting OC (output code). Recently, boosting was successfully used in many study fields such as object detection and tracking. It is one kind of large margin classifiers for binary classification problems and also efficient for on-line learning. However, face recognition is a typical multi-class problem. Hence, it is difficult to use boosting in face recognition, especially in an online version. In our work, we combine online boosting and OC algorithm to solve real-time online multi-class classification problems. We perform online boosting OC on real-world experiments: face recognition in continuous video stream, and the results show that our algorithm is accurate and robust.