Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models

In tracking face and facial actions of unknown people, it is essential to take into account two components of facial shape variations: shape variation between people and variation caused by different facial actions such as facial expressions. This paper presents a monocular method of tracking faces and facial actions using a multilinear face model that treats interpersonal and intrapersonal shape variations separately. We created this method using a multilinear face model by integrating two different frameworks: particle filter-based tracking for time-dependent facial action and pose estimation and incremental bundle adjustment for person-dependent shape estimation. This unique combination together with multilinear face models is the key to tracking faces and facial actions of arbitrary people in real time with no pre-learned individual face models. Experiments using real video sequences demonstrate the effectiveness of our method.