Subject-Independent Facial Expression Recognition with Biologically Inspired Features

Despite of much research for facial expression recognition, recognizing facial expressions across different persons is still a challenging computer vision task. However, facial expression analysis seems naturally for human visual system. Motivated by visual biology, this paper proposes an invariant feature extraction method for subject-independent facial expression recognition. In particular, we extract the biologically inspired facial features using extended visual cortex model-HMAX which consist of a template matching and a maximum pooling operation. We carefully organized the facial features and achieve subject-independent facial expression recognition using a sparse representation based classifier. The experiments on Yale database and JAFFE database demonstrate the significance of our proposed method for subject-independent facial expression recognition.