Modelling Facial Behaviours

We consider the problem of learning how a person's face behaves in a long video sequence, with the aim of synthesising convincing sequences demonstrating the same behaviours. We describe a novel approach to segment a sequence into short sections, each representing a distinct action (or a part of an action). These sections are grouped and a model of the variability of the action learnt. A variable length Markov model is trained on the sequence of such actions to learn the temporal relationships. The result is a system that can generate realistic sequences of an individual face.

• Type: Conference
• Year: 2002
• Where: BMVC
• Authors: Franck Bettinger, Timothy F. Cootes, Christopher J. Taylor
• Keywords: BMVC 2002, Computer Vision, Convincing Sequences, Long Video Sequence, Person's Face Behaves