Independent Subspace of Dynamic Gabor Features for Facial Expression Classification

In this paper, the Gabor filter is studied and further expanded for temporal facial expression analysis. Originally, the Gabor feature describes both spatial and frequency characteristics of 2D images. The prominent of the theorem has been validated in research communities for a decade due to its similarity to the human perception system. The performance of the filter in the existing research gives convincing results on recognizing the human emotions by using a still image. However, the previous research neglects the fact that the understanding of human facial expression of emotions is associated by the dynamic relation, which the motion of expression must be witnessed. Therefore, we propose the novel temporal features by deriving the dynamic of Gabor features in the temporal template representations. Then, we decompose the features onto discriminative subspace for estimating the emotion class.