Face Detecting And Tracking On Intelligent Surveillance System

This thesis presents a method for building a face detecting and tracking system. We carry up a deep research into the training and applying of face classifiers based on the statistical method,which is widely used in face detecting field. In the meantime we build a face tracking system using the CamShift algorithm based on the hue information of skin color,which is a robust nonparametric technique for climbing density gradients to find the mode(peak) of probability distributions. The face detecting and tracking system we design is qualified for real video surveillance project.This thesis begins with an instruction to the development of face detecting and tracking technology and follows a general description of several primary methods for face detecting and tracking. Then we carry up a deep research into the face classifiers using Adaboost based on extended Haar-like features; build the face color histogram by hue information, and track the face by CamShift algorithm. The main research innovations and contributions are summarized as follows:1,We carry up a deep research into training face classifiers for frontal faces and profile faces detection using the AdaBoost based on the extended Haar-like features. And we use two classifiers simultaneously for face detecting. The profile faces classifier can efficiently detect faces with larger lateral rotation,which compensates the shortcoming of the frontal face classifier. The combination of frontal and profile face classifiers helps improve the detection result a lot,and makes the system more robust.2,We explore the CamShift algorithm and put it into the face tracking system. We build the hue histogram on the detected face region and then...