Incremental learning of dynamical models of faces

Cyril Charron, Yulia Hicks, Peter Hall, Darren Cosker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Active Appearance Models (AAM) are a useful and popular tool for modelling facial variations. They have been used in face tracking, recognition and synthesis applications. For modelling facial dynamics of speech, they have been used in conjunction with Hidden Markov Models (HMM). However, the high dimensionality of the training data and of the resulting AAMs leads to long learning time of HMMs and thus imposes serious limitations on their joint use. Here, we propose a new method for learning HMMs of facial dynamics incrementally. Our algorithm is fully unsupervised and can be used for on-line learning as new data becomes available. Another important feature of our algorithm is the automatic choice of the number of states in the model. We show in experiments an improvement in learning speed of three orders of magnitude. Finally, we demonstrate the quality of the learned HMMs by generating video footage of a talking face.

Original languageEnglish
Title of host publicationBritish Machine Vision Conference, BMVC 2009 - Proceedings
PublisherBritish Machine Vision Association
ISBN (Print)1901725391, 9781901725391
DOIs
Publication statusPublished - 1 Jan 2009
Event2009 20th British Machine Vision Conference, BMVC 2009 - London, UK United Kingdom
Duration: 7 Sep 200910 Sep 2009

Publication series

NameBritish Machine Vision Conference, BMVC 2009 - Proceedings

Conference

Conference2009 20th British Machine Vision Conference, BMVC 2009
CountryUK United Kingdom
CityLondon
Period7/09/0910/09/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Incremental learning of dynamical models of faces'. Together they form a unique fingerprint.

Cite this