Robust Facial Expression Recognition via Sparse Representation and Multiple Gabor filters

Facial expressions recognition plays important role in human communication. It has become one of the most challenging tasks in the pattern recognition field. It has many applications such as: human computer interaction, video surveillance, forensic applications, criminal investigations, and in many other fields. In this paper we propose a method for facial expression recognition (FER). This method provides new insights into two issues in FER: feature extraction and robustness. For feature extraction we are using sparse representation approach after applying multiple Gabor filter and then using support vector machine (SVM) as classifier. We conduct extensive experiments on standard facial expressions database to verify the performance of proposed method. And we compare the result with other approach.