Facial expression analysis using a sparse representation based space model

With the development of information technologies, facial expression analysis becomes more and more essential to human computer interaction (HCI). A natural way to analysis facial expression is derived from the study on human emotion which is regarded as the intrinsic origin of facial expression. Another issue for facial expression analysis is to extract substaintial facial features that correspond with visual perception system. Based on these observations, we present a sparse representation based space model for facial expression analysis which applies Gabor filters to extract facial features. The sparse representation based facial expression space model is induced from human emotion space and then can describe mixture facial expressions which are usual in daily life. Experiments on JAFFE database demonstrate the validity of the proposed facial expression space model.