Dynamic Facial Expression Recognition Using A Bayesian Temporal Manifold Model

In this paper, we propose a novel Bayesian approach to modelling temporal transitions of facial expressions represented in a manifold, with the aim of dynamical facial expression recognition in image sequences. A generalised expression manifold is derived by embedding image data into a low dimensional subspace using Supervised Locality Preserving Projections. A Bayesian temporal model is formulated to capture the dynamic facial expression transition in the manifold. Our experimental results demonstrate the advantages gained from exploiting explicitly temporal information in expression image sequences resulting in both superior recognition rates and improved robustness against static frame-based recognition methods.