Incremental learning of dynamical models of faces

Cyril Charron, Yulia Hicks, Peter Hall, Darren Cosker

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding


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
Publication statusPublished - 1 Jan 2009
Event2009 20th British Machine Vision Conference, BMVC 2009 - London, UK United Kingdom
Duration: 7 Sept 200910 Sept 2009

Publication series

NameBritish Machine Vision Conference, BMVC 2009 - Proceedings


Conference2009 20th British Machine Vision Conference, BMVC 2009
Country/TerritoryUK United Kingdom

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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