Adaptive 3D Facial Feature Tracking Combining Robust Feature with Online Learning
综合鲁棒特征和在线学习的自适应三维人脸多特征跟踪

An algorithm based on robust feature combining edge strength and raw intensity and online appearance model fitting was proposed to track head pose and facial actions in video. A 3D parameterized model, CAND)DE model, was used to model the face and facial expression, a weak perspective projection method was used to model the head pose, an adaptive appearance model was built on shape free intensity and edge texture, and then a gradient decent model fitting algorithm was taken to track parameters of head pose and facial actions. Experiments demonstrate that the algorithm is more robust than using only intensity especially when the lighting condition and facial expression is complicated.