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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 |
Appears in Collections: | Staff Publications |
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