An extensible framework for facial motion tracking

Xiaolu Shen, Xuetao Feng, Jungbae Kim, Hui Zhang, Youngkyoo Hwang, Ji-yeun Kim

Abstract: Facial motion tracking is a challenging task because of highly flexible head pose and facial expression. An extensible tracking framework is proposed in this paper. Within the framework, proper models are selected according to requirements and restrictions of the application, and different trackers can be constructed to handle different tasks. Experimental result shows that our tracker outperforms the existing commercial software.