Enhanced dictionary pair learning sparse representation model for facial expression classification

摘要

Facial expression recognition (FER) is a challenging task in the community of affect analysis and pattern recognition. In this paper, we propose a novel framework, namely Enhanced Dictionary Pair Learning Sparse Representation (EDPLSR), for facial expression recognition. The key idea behind our model is that it jointly learns a synthesis dictionary as well as an analysis dictionary, which require that all coding vectors should be group sparse. Furthermore, inspired by the observation that the geometrical information of the data is discriminative, a manifold regularization term is introduced to obtain smoothly vary sparse representations along the geodesics of data manifold. This is distinctive from most of the existing approaches which fail to consider the geometrical structure of data space. The experimental results demonstrate the effectiveness of our method.