Adaboost And Particle Filter-based Face Detection And Tracking

With the development of computer science and pattern recognition, face detection and tracking has been a hot issue in computer vision, and has been widely used in various aspects, such as human face recognition, intelligent surveillance, human-computer interaction, etc. This thesis mainly focuses on the application of AdaBoost in face detection and the application of particle filter in face tracking. The main studies are showed as follows:Under the conditions of the complex background and variable illumination, the YCbCr and rgb color spaces are used to realize the skin-color segmentation, it can reduce the error rate comparing with using a single color space; after the skin-color pretreatment, using AdaBoost algorithm to test and verify. Conduct research in the adjacent proportation coefficient of the search window effect on the face detection using AdaBoost algorithm, and then propose a adaptive strategy. The new method can improve the efficiency and reduce the error rate of the traditional AdaBoost algorithm.The research on Mean Shift algorithm commenly used in target tracking is conducted, and then an adaptive window size adjustment strategy is used to solve the fixed window size problem. To solve the problem of losing track after occlusion, kalman filter is studied to predict. Particle filter which is widely used in the real world is then studied, for particle filter based on single color feature in the complex environment easily lead to tracking failure, this paper proposes a particle filter algorithm based on the integration of color feature and histogram of oriented gradients, and the new particle filter can improve the robustness of algorithms in tracking.At last,The new algorithm based on AdaBoost and particle filter is used to realize the human face detection and tracking. Inter difference algorithm is introduced to avoid the waste of efficiency when using AdaBoost to realize face detection; Once the face has been detected, particle filter algorithm is introduced to realize the face tracking. In order to avoid the particle degeneracy, Mean Shift algorithm is incorporated into the particle filter. Based on the needs of the intelligent face recognition, a framework for face recognition is designed, and the proposed algorithm is used to realize the part of face detection and tracking.