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Side information for face completion : a robust PCA approach

Xue, Niannan; Deng, Jiankang; Cheng, Shiyang; Panagakis, Yannis; Zafeiriou, Stefanos (2019-03-04)

 
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https://doi.org/10.1109/TPAMI.2019.2902556

Xue, Niannan
Deng, Jiankang
Cheng, Shiyang
Panagakis, Yannis
Zafeiriou, Stefanos
Institute of Electrical and Electronics Engineers
04.03.2019

N. Xue, J. Deng, S. Cheng, Y. Panagakis and S. Zafeiriou, "Side Information for Face Completion: A Robust PCA Approach," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 10, pp. 2349-2364, 1 Oct. 2019, doi: 10.1109/TPAMI.2019.2902556

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https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/TPAMI.2019.2902556
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https://urn.fi/URN:NBN:fi-fe2020051229393
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Abstract

Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of various visual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawback that can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for the RPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UV completion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) to extract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in case of large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and eight real-world datasets to verify their effectiveness.

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