基于概率图模型的人脸多特征跟踪

Probabilistic Graphical Model for Multiple Facial Feature Tracking

同时跟踪具有丰富表情的人脸多个特征是一个有挑战性的问题 提出了一个基于时空概率图模型的方法 在时间域上 ,使用几个相互独立的Condensation类型的粒子滤波器分别跟踪人脸的每个特征 粒子滤波对独立的视觉跟踪问题非常有效 ,但是多个独立的跟踪器忽视了人脸的空间约束和人脸特征间的自然相互联系 ;在空间域上 ,事先从人脸表情库中学习人脸特征轮廓的相互关系 ,使用贝叶斯推理 -信任度传播算法来对人脸特征的轮廓位置进行求精 实验结果表明 ,文中算法可以在帧间运动较大的情况下 。

Tracking multiple facial features simultaneously is a challenge when rich expressions are presented on a face Several independent condensation-style particle filters are utilized to track each facial feature in temporal domain Particle filters are very effective for visual tracking problems, however multiple independent trackers ignore the natural relationships among facial features We use Bayesian inference-belief propagation to infer each facial feature's contour in spatial domain, taking into consideration the previously extracted relationships among contours of facial features which are organized as a large facial expression database Experimental results show that our algorithm is robust