tailieunhanh - Báo cáo hóa học: " Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking | EURASIP Journal on Applied Signal Processing 2005 13 2091-2100 2005 Hindawi Publishing Corporation Spatio-Temporal Graphical-Model-Based Multiple Facial Feature Tracking Congyong Su College of Computer Science Zhejiang University Hangzhou 310027 China Email su@ Li Huang College of Computer Science Zhejiang University Hangzhou 310027 China Email lihuang@ Received 1 January 2004 Revised 20 February 2005 It is challenging to track multiple facial features simultaneously when rich expressions are presented on a face. We propose a two-step solution. In the first step several independent condensation-style particle filters are utilized to track each facial feature in the temporal domain. Particle filters are very effective for visual tracking problems however multiple independent trackers ignore the spatial constraints and the natural relationships among facial features. In the second step we use Bayesian inference belief propagation to infer each facial feature s contour in the spatial domain in which we learn the relationships among contours of facial features beforehand with the help of a large facial expression database. The experimental results show that our algorithm can robustly track multiple facial features simultaneously while there are large interframe motions with expression changes. Keywords and phrases facial feature tracking particle filter belief propagation graphical model. 1. INTRODUCTION Multiple facial feature tracking is very important in the computer vision field it needs to be carried out before videobased facial expression analysis and expression cloning. Multiple facial feature tracking is also very challenging because there are plentiful nonrigid motions in facial features besides rigid motions in faces. Nonrigid facial feature motions are usually very rapid and often form dense clutter by facial features themselves. Only using traditional Kalman filter is inadequate because it is based on Gaussian density and works .