Design of unsupervised facial expression animation based on geometric grid measurement

Many actual application images in the real world are formed by high dimensional data in most cases, while the manifold learning algorithm can explore the nonlinear information hidden in these high dimensional data. As most of manifold learning algorithms can only be defined in training cluster, it is impossible to project the sample on the lower dimensional space. In the thesis, we introduce a kind of double manifold algorithm based on LLE and Isomap. Different from the traditional LLE algorithm, our algorithm learns two kinds of manifold information in which one group of data relates to many types and it compares two kinds of single LLE algorithm and Isomap algorithm through the setting of the appropriate nearest neighbour number K. No matter for the recognition rate or running time, it is obviously superior to the other two kinds of algorithms and it can effectively achieve the estimation of facial expression and significantly reduce the computation complexity.