Analysis of Head and Facial Gestures Using Facial Landmark Trajectories

Abstract

Automatic analysis of head and facial gestures is a significant and challenging research area for human-computer interfaces. We propose a robust face-and head gesture analyzer. The analyzer exploits trajectories of facial landmark positions during the course of the head gesture or facial expression. The trajectories themselves are obtained as the output of an accurate feature detector and tracker algorithm, which uses a combination of appearance- and model-based approaches. A multi-pose deformable shape model is trained in order to handle shape variations under varying head rotations and facial expressions. Discriminative observation symbols extracted from the landmark trajectories drive a continuous HMM with mixture of Gaussian outputs and is used to recognize a subset of head gestures and facial expressions. For seven gesture classes we achieve 86.4 % recognition rate.