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ACCV
2009
Springer

Head Pose Estimation Based on Manifold Embedding and Distance Metric Learning

9 years 2 months ago
Head Pose Estimation Based on Manifold Embedding and Distance Metric Learning
In this paper, we propose an embedding method to seek an optimal low-dimensional manifold describing the intrinsical pose variations and to provide an identity-independent head pose estimator. In order to handle the appearance variations caused by identity, we use a learned Mahalanobis distance to seek optimal subjects with similar manifold to construct the embedding. Then, we propose a new smooth and discriminative embedding method supervised by both pose and identity information. To estimate pose of a head new image, we first find its knearest neighbors of different subjects, and then embed it into the manifold of the subjects to estimate the pose angle. The empirical study on the standard databases demonstrates that the proposed method achieves high pose estimation accuracy.
Xiangyang Liu, Hongtao Lu, Daqiang Zhang
Added 02 Sep 2010
Updated 02 Sep 2010
Type Conference
Year 2009
Where ACCV
Authors Xiangyang Liu, Hongtao Lu, Daqiang Zhang
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