Fully automatic, real-time detection of facial gestures from generic video

Abstract

A technique for the detection of facial gestures from low resolution video sequences is presented. The technique builds upon the automatic 3D head tracker formulation of [M. La Cascia et al., 2000]. The tracker is based on the registration of a texture-mapped cylindrical model. Facial gesture analysis is performed in the texture map by assuming that the residual registration error can be modeled as a linear combination of facial motion templates. Two formulations are proposed and tested. In one formulation, the head and facial motion are estimated in a single, combined linear system. In the other formulation, head motion and then facial motion are estimated in a two-step process. The two-step approach significantly yields better accuracy in facial gesture analysis. The system is demonstrated in detecting two types of facial gestures: mouth opening and eyebrows raising. On a dataset with lots of head motion, the two-step algorithm achieved a recognition accuracy of 70% for the mouth opening and an accuracy of 66% for eyebrows raising gestures. The algorithm can reliably track and classify facial gestures without any user intervention and runs in real-time.