Abstract. This paper is concerned with capturing the dynamics of facial expression. The dynamics of facial expression can be described as the intensity and timing of a facial expression and its formation. To achieve this we developed a technique that can accurately classify and differentiate between subtle and similar expressions, involving the lower face. This is achieved by using Local Linear Embedding (LLE) to reduce the dimensionality of the dataset and applying Support Vector Machines (SVMs) to classify expressions. We then extended this technique to estimate the dynamics of facial expression formation in terms of intensity and timing.
For example, Reilly REF [22] used Local Linear Embedding (LLE) and Kernel Principal Component (KPCA) to reduce the feature dimensions, and then used SVM to train the classifiers of different intensity levels.
Investigating the Dynamics of Facial Expression
ISVC
Computer Science