Landmark manifold: Revisiting the Riemannian manifold approach for facial emotion recognition
Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose a novel method, here called the landmark manifold, that shows the possibility to achieve competitive performance by facial landmark information alone. Through experiments on the well-known dataset: marked Cohn-Kanade extended facial emotion dataset (CK+), we show that with accurate facial landmarks, our simple approach is fast to run and can achieve competitive performance with enormously expensive methods.