Research On Human Face Detection And Tracking Algorithms Based On Adaboost

Face detection and tracking is not only a preliminary task for face information processing, but also a key step to it. In addition to the well-known face recognition, the fields of the face image processing usually include expression recognition, intelligent human-computer interaction and video surveillance, and almost every field is inseparable from the face detection and tracking. This paper based on the latest research results for face detection and tracking at home and abroad, systematic analyses and study the face detection and tracking algorithms. According to problems, such as the face’s variability and the tracker may lose the target easily in complicated environment, algorithm based on improved AdaBoost is proposed for face detection and tracking, which is verified real-time performance and robustness under the experimental condition. The main research results of this paper can be concluded as follows:1. Research a large number of classic face detection algorithms, we combined the face detection algorithm based on AdaBoost with color filter method. By this way, the start-up times of the AdaBoost algorithm goes down and the efficiency of the face detection would be improved. In addition, we reduced the face detection error rate through removing redundant Harr-like rectangle features and inreasing the training sample.2. Study and actual test a large number of tracking algorithms, we focus on the following two algorithms:the CAMSHIFT and Particle Filter algorithm. According to the complex background and occlusion in the process of face tracking, we put forward that under normal circumstances, the global tracker based on CAMSHIFT is called for tracking, once the face information below a specific threshold value, the local tracker base on Particle Filter is invoked to face tracking.3. We simulate and test the face detection and tracking algorithm we designed in this paper under certain hardware and software environment, at the same time, we compared the proposed algorithm with previous algorithm in time complexity and the actual effect on the face detection and tracking.