factorization for facial expression analysis and synthesis
B. Abboud and Franck Davoine
IEE Proceedings - Vision, Image & Signal Processing
This paper addresses the issue of face representations for facial expression recognition and synthesis. In this context, a global active appearance model is used in conjunction with two bilinear factorization models to separate expression and identity factors from the global appearance parameters.
Although active appearance models and bilinear modelling are not new concepts, the main contribution of this paper consists in combining both techniques to improve facial expression
recognition and synthesis (control).
Indeed, facial expression recognition is performed through Linear Discriminant Analysis of the global appearance parameters extracted by active appearance model (AAM) search. Results are compared to the ones obtained for the same training and test images using classification of the
expression factors extracted by bilinear factorization. This experiment highlights the advantages of bilinear factorization.
Finally, we propose to exploit bilinear factorization to synthesize facial expressions through replacement of the extracted expression factors. This yields very interesting synthesis performances in terms of visual quality of the synthetic faces. Indeed, synthetic open mouths reconstruction either with or without appearing teeth is of better quality than with classical linear regression based synthesis.