Smooth Multi-Manifold Embedding for Robust Identity-Independent Head Pose Estimation

In this paper, we propose a supervised Smooth Multi-Manifold Embedding (SMME) method for robust identity-independent head pose estimation. In order to handle the appearance variations caused by identity, we consider the pose data space as multiple manifolds in which each manifold characterizes the underlying subspace of subjects with similar appearance. We then propose a novel embedding criterion to learn each manifold from the exemplar-centered local structure of subjects. The experiment results on the standard databases demonstrates that the SMME is robust to variations of identities and achieves high pose estimation accuracy.

• Type: Conference
• Year: 2009
• Where: CAIP
• Authors: Xiangyang Liu, Hongtao Lu, Heng Luo
• Keywords: CAIP 2009, Head Pose Estimation, Pattern Recognition, Pose Estimation, Smooth Multi-Manifold Embedding