Manifold based analysis of facial expression

We propose a novel approach for modeling, tracking and recognizing facial expressions. Our method works on a low dimensional expression manifold, which is obtained by Isomap embedding. In this space, facial contour features are first clustered, using a mixture model. Then, expression dynamics are learned for tracking and classification. We use ICondensation to track facial features in the embedded space, while recognizing facial expressions in a cooperative manner, within a common probabilistic framework. The image observation likelihood is derived from a variation of the Active Shape Model (ASM) algorithm. For each cluster in the lowdimensional space, a specific ASM model is learned, thus avoiding incorrect matching due to non-linear image variations. Preliminary experimental results show that our probabilistic facial expression model on manifold significantly improves facial deformation tracking and expression recognition.

• Type: Journal
• Year: 2006
• Where: IVC
• Authors: Ya Chang, Changbo Hu, Rogerio Feris, Matthew Turk
• Keywords: Facial Contour Features, Facial Expression, IVC 2006, Low Dimensional Expression