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CAIP
2009
Springer

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

12 years 2 days ago
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.
Xiangyang Liu, Hongtao Lu, Heng Luo
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where CAIP
Authors Xiangyang Liu, Hongtao Lu, Heng Luo
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