Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2009.2038765
Title: Misalignment-robust face recognition
Authors: Yan, S. 
Wang, H.
Liu, J.
Tang, X.
Huang, T.S.
Keywords: Face recognition
Spatial misalignments
Subspace learning
Issue Date: Apr-2010
Citation: Yan, S., Wang, H., Liu, J., Tang, X., Huang, T.S. (2010-04). Misalignment-robust face recognition. IEEE Transactions on Image Processing 19 (4) : 1087-1096. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2009.2038765
Abstract: Subspace learning techniques for face recognition have been widely studied in the past three decades. In this paper, we study the problem of general subspace-based face recognition under the scenarios with spatial misalignments and/or image occlusions. For a given subspace derived from training data in a supervised, unsupervised, or semi-supervised manner, the embedding of a new datum and its underlying spatial misalignment parameters are simultaneously inferred by solving a constrained $\ell-{1}$ norm optimization problem, which minimizes the $\ell-{1}$ error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and/or image occlusions all validate the effectiveness of our proposed general formulation for misalignment-robust face recognition. © 2006 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/56644
ISSN: 10577149
DOI: 10.1109/TIP.2009.2038765
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