Multi-view Facial Expression Recognition Using Parametric Kernel Eigenspace Method Based on Class Features

Automatic facial expression recognition is an important technique for interaction between humans and machines such as robots or computers. In particular, pose invariant facial expression recognition is needed in an automatic facial expression system because frontal faces are not always visible in real situations. The present paper introduces a multi-view method for recognizing facial expressions using a parametric kernel eigenspace method based on class features (pKEMC). We first describe pKEMC that finds the manifold of data patterns in each class on a non-linear discriminant subspace for separating multiple classes. Then, we apply pKEMC for pose-invariant facial expression recognition. We also utilize facial-component-based representation to improve the robustness to pose variation. We carried out the validation of our method on a Multi-PIE database. The results show that our method has high discrimination accuracy and provides an effective means to recognize multi-view facial expressions.