CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2013 vol.35 Issue No.06 - June
Issue No.06 - June (2013 vol.35)
Ognjen Rudovic , Imperial College London, London, UK
Maja Pantic , Imperial College, London, UK
Ioannis Patras , Queen Mary University, London, UK
We propose a method for head-pose invariant facial expression recognition that is based on a set of characteristic facial points. To achieve head-pose invariance, we propose the Coupled Scaled Gaussian Process Regression (CSGPR) model for head-pose normalization. In this model, we first learn independently the mappings between the facial points in each pair of (discrete) nonfrontal poses and the frontal pose, and then perform their coupling in order to capture dependences between them. During inference, the outputs of the coupled functions from different poses are combined using a gating function, devised based on the head-pose estimation for the query points. The proposed model outperforms state-of-the-art regression-based approaches to head-pose normalization, 2D and 3D Point Distribution Models (PDMs), and Active Appearance Models (AAMs), especially in cases of unknown poses and imbalanced training data. To the best of our knowledge, the proposed method is the first one that is able to deal with expressive faces in the range from $(-45^\circ)$ to $(+45^\circ)$ pan rotation and $(-30^\circ)$ to $(+30^\circ)$ tilt rotation, and with continuous changes in head pose, despite the fact that training was conducted on a small set of discrete poses. We evaluate the proposed method on synthetic and real images depicting acted and spontaneously displayed facial expressions.
Face recognition, Head, Solid modeling, Active appearance model, Estimation, Training, Magnetic heads,Gaussian process regression, Multiview/pose-invariant facial expression/emotion recognition, head-pose estimation
Ognjen Rudovic, Maja Pantic, Ioannis Patras, "Coupled Gaussian processes for pose-invariant facial expression recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 6, pp. 1357-1369, June 2013, doi:10.1109/TPAMI.2012.233