Study On Algorithms Of Face Detecting And Tracking In Video

The face detection and tracking are one of the key techniques in various facial processing algorithms. The research fields of facial image processing include face recognition, gesture estimation, facial expression recognition, video monitoring and so on. And nearly all of these are involved in the area of face detection and face tracking. Based on the collection and analysis of numerous the domestic and international academic thesis and research reports on face detection and tracking in recent years, face detection and tracking algorithms is lucubrated. According to the previous research achievements on face detection and tracking, a face detection algorithm based on Adaboost and CamShift has been realized. Aiming at the experiment conditions, crucial improvements have been achieved by learning from some traditional and classical algorithms of face detection and tracking. The study of this paper focused on the following aspects:1. Aiming at the poor rate of side face detection by traditional Adaboost algorihtm, some related improvements to Adaboost algorihtm were proposed. Firstly, few key Haar-Like features from a large feature set were selected to generate an effective strong classifier. Secondly, cascades these single strong classifier into a more complex cascade classifier. On that basis, the frontal face classifier and side face classifier were trained individually and the detection results of frontal face with side face were inosculated to obtain face region. Afterwards, skin color was further validated on the face region to enhance the robustness of the algorithm and reduce the false alarm rate. The results from experiments demonstrated the high speed of test and good real-time. With the CMU face test library on this algorithm, the detection rate can reach 82.7%.2. In this paper, the face detection algorithm on skin color was comparative analyzed with the algorithm based on Adaboost. Aiming at the issue that the Adaboost algorithm is not effective when faces rotated or being blocked out. In order to overcome this shortcoming, a method to combine the Adaboost algorithm with face detection algorithm was presented in this paper.3. The Adaboost algorithm and CamShift algorithm were combined well in software development tool such as Visual C++. Firstly, the tracking window based on face region was initialized in detecting process. Then, color hue information model was established to track the follow-up frame. Presented the experiment results, it is showed that the algorithm needs less calculating amounts with fast tracking speed, and without influenced by irregular movement of face. And the phenomenon of face tracking error does not occur when other people appeared on the scene. Furthermore, track loosing does not happen when the face kept out partly. Therefore, the matching and accuracy of the tracking algorithm has been improved greatly. The feasibility and effectiveness of this improved algorithm was verified.