In this paper, we present our latest work on facial expression analysis, synthesis and face recognition. The advent of new technologies that allow the capture of massive amounts of high resolution, high frame rate face data, leads us to propose data-driven face models that accurately describe the appearance of faces under unknown pose and illumination conditions as well as to track subtle geometry changes that occur during expressions. In this paper, we also demonstrate our results for expression transfer among different subjects. We reduce the dimensionality of our data onto a lower dimensional space manifold and then decompose it into style and content parameters. This allows us to transfer subtle expression information (in the form of a style vector) between individuals to synthesize new expressions, as well as smoothly morph geometry and motion. Finally, we demonstrate the accuracy of our face modeling methods through an integrated example of image-driven re-targeting and relighting of facial expressions, where transfer of expression and illumination information between different individuals is possible.
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