Dimensionality reduction for computer facial animation

摘要

This paper describes the usage of dimensionality reduction techniques for computer facial animation. Techniques such as Principal Components Analysis (PCA), Expectation–Maximization (EM) algorithm for PCA, Multidimensional Scaling (MDS), and Locally Linear Embedding (LLE) are compared for the purpose of facial animation of different emotions. The experimental results on our facial animation data demonstrate the usefulness of dimensionality reduction techniques for both space and time reduction. In particular, the EMPCA algorithm performed especially well in our dataset, with negligible error of only 1–2%.