Side information for face completion: a robust PCA approach
File(s)
Accepted version
Author(s)
Deng, Jiankang
Xue, Niannan
Cheng, Shiyang
Panagakis, ioannis
Zafeiriou, Stefanos
Type
Journal Article
Abstract
Robust principal component analysis (RPCA) is a powerful method for learning low-rank feature representation of variousvisual data. However, for certain types as well as significant amount of error corruption, it fails to yield satisfactory results; a drawbackthat can be alleviated by exploiting domain-dependent prior knowledge or information. In this paper, we propose two models for theRPCA that take into account such side information, even in the presence of missing values. We apply this framework to the task of UVcompletion which is widely used in pose-invariant face recognition. Moreover, we construct a generative adversarial network (GAN) toextract side information as well as subspaces. These subspaces not only assist in the recovery but also speed up the process in caseof large-scale data. We quantitatively and qualitatively evaluate the proposed approaches through both synthetic data and fivereal-world datasets to verify their effectiveness.
Date Issued
2019-10-01
Date Acceptance
2019-02-23
Citation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41 (10), pp.2349-2364
ISSN
0162-8828
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2349
End Page
2364
Journal / Book Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume
41
Issue
10
Copyright Statement
This paper is embargoed until publication.
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Engineering & Physical Science Research Council (E
Engineering & Physical Science Research Council (EPSRC)
Grant Number
EP/N007743/1
EP/S010203/1
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
RPCA
GAN
side information
UV completion
face recognition
in the wild
LOW-RANK MATRIX
REPRESENTATION
RECOGNITION
ALGORITHM
0801 Artificial Intelligence and Image Processing
0806 Information Systems
0906 Electrical and Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status
Published
Date Publish Online
2019-03-04