By Topic

Multi-manifold modeling for head pose estimation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Xiangyang Liu ; Department of Computer Science and Engineering, Shanghai Jiao Tong University, 200240, China ; Hongtao Lu ; Wenbin Li

In this paper, we study the identity-independent head pose estimation problem, in order to handle the appearance variations, we consider the pose data lying on multiple manifolds. We present a novel manifold clustering method to construct multiple manifolds each of which characterizes the underlying subspace of some subjects. We first construct a set of n-simplexes of subjects by using the similarity of pose images. Then, we present a supervised method to obtain a low-dimensional manifold embedding for each n-simplex. Finally, we propose the K-manifold clustering method, integrating manifold embedding and clustering, to make each learned manifold with unique geometric structure. The experimental results on a standard database demonstrate that our method is robust to variations of identities and achieves high pose estimation accuracy.

Published in:

2010 IEEE International Conference on Image Processing

Date of Conference:

26-29 Sept. 2010