Deformable model-based face shape and motion estimation

Abstract

We describe a model-based approach to human face shape and motion estimation using a deformable model framework in which optical flow information has been integrated. Our 3-D deformable face model uses a small number of parameters to describe a rich variety of face shapes and facial expressions. A detailed face model allows for the handling of self-occlusion and the correction of error accumulation in tracking by aligning the expected location of face features with those found in the input images. We present experiments in which we employ Kalman filtering to estimate the shape and motion of a face from image sequences of four subjects. The face is successfully tracked even in the presence of large head rotations.