This thesis researches techniques for modelling static facial expressions, as well as the dynamics of continuous
facial motion. We demonstrate how static and dynamic properties of facial expressions can be represented within a linear
and nonlinear context, respectively. These two representations do not act in isolation, but are mutually reinforcing in
conceding a cohesive framework for the analysis, animation, and manipulation of expressive faces. We derive a basis for
the linear space of expressions through Principal Components Analysis (PCA). We introduce and formalize the notion
of "expression manifolds", manifolds residing in PCA space that model motion dynamics for semantically similar expressions.
We then integrate these manifolds into an animation workflow by performing Nonlinear Dimensionality Reduction (NLDR) on the
expression manifolds. This operation yields expression maps that encode a wealth of information relating
to complex facial dynamics, in a low dimensional space that is intuitive to navigate and efficient to manage.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/10428 |
| Date | 25 July 2008 |
| Creators | Bastani, Hanieh |
| Contributors | Fiume, Eugene |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 14431601 bytes, application/pdf |
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