Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2008.2006400
Title: Synchronized submanifold embedding for person-independent pose estimation and beyond
Authors: Yan, S. 
Wang, H.
Fu, Y.
Yan, J.
Tang, X.
Huang, T.S.
Keywords: 3-D pose estimation
Age estimation
Manifold learning
Simplex
Subspace learning
Issue Date: 2009
Source: Yan, S., Wang, H., Fu, Y., Yan, J., Tang, X., Huang, T.S. (2009). Synchronized submanifold embedding for person-independent pose estimation and beyond. IEEE Transactions on Image Processing 18 (1) : 202-210. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2008.2006400
Abstract: Precise 3-D head pose estimation plays a significant role in developing human-computer interfaces and practical face recognition systems. This task is challenging due to the particular appearance variations caused by pose changes for a certain subject. In this paper, the pose data space is considered as a union of submanifolds which characterize different subjects, instead of a single continuous manifold as conventionally regarded. A novel manifold embedding algorithm dually supervised by both identity and pose information, called snchronized submanifold embedding (SSE), is proposed for person-independent precise 3-D pose estimation, which means that the testing subject may not appear in the model training stage. First, the submanifold of a certain subject is approximated as a set of simplexes constructed using neighboring samples. Then, these simplexized submanifolds from different subjects are embedded by synchronizing the locally propagated poses within the simplexes and at the same time maximizing the intrasubmanifold variances. Finally, the pose of a new datum is estimated as the propagated pose of the nearest point within the simplex constructed by its nearest neighbors in the dimensionality reduced feature space. The experiments on the 3-D pose estimation database, CHIL data for CLEAR07 evaluation, and the extended application for age estimation on FG-NET aging database, demonstrate the superiority of SSE over conventional regression algorithms as well as unsupervised manifold learning algorithms. © 2008 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/57580
ISSN: 10577149
DOI: 10.1109/TIP.2008.2006400
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