A simple geometric-based descriptor for facial expression recognition

The identification of facial expressions with human emotions plays a key role in non-verbal human communication and has applications in several areas. In this work, we propose a descriptor based on areas and angles of triangles formed by the landmarks from face images. We test these descriptors for facial expression recognition by means of two different approaches. One is a dynamic approach where recognition is performed by a Conditional Random Field (CRF) classifier. The other approach is an adaptation of the k-Nearest Neighbors classifier called Citation-kNN in which the training examples come in the form of sets of feature vectors. An analysis of the most discriminative landmarks for the CRF approach is presented. We compare both methodologies, analyse their similarities and differences. Comparisons with other state-of-the-art techniques on the CK+ dataset are shown. Even though both methodologies are different from each other, the descriptor remains robust and precise in the recognition of expressions.