Recovering joint and individual components in facial data

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Title: Recovering joint and individual components in facial data
Authors: Sagonas
Ververas
Panagakis
Zafeiriou, S
Item Type: Journal Article
Abstract: A set of images depicting faces with different expressions or in various ages consists of components that are shared across all images (i.e., joint components) imparting to the depicted object the properties of human faces as well as individual components that are related to different expressions or age groups. Discovering the common (joint) and individual components in facial images is crucial for applications such as facial expression transfer and age progression. The problem is rather challenging when dealing with images captured in unconstrained conditions in the presence of sparse non-Gaussian errors of large magnitude (i.e., sparse gross errors or outliers) and contain missing data. In this paper, we investigate the use of a method recently introduced in statistics, the so-called Joint and Individual Variance Explained (JIVE) method, for the robust recovery of joint and individual components in visual facial data consisting of an arbitrary number of views. Since the JIVE is not robust to sparse gross errors, we propose alternatives, which are (1) robust to sparse gross, non-Gaussian noise, (2) able to automatically find the individual components rank, and (3) can handle missing data. We demonstrate the effectiveness of the proposed methods to several computer vision applications, namely facial expression synthesis and 2D and 3D face age progression ‘in-the-wild’.
Issue Date: 1-Nov-2018
Date of Acceptance: 5-Oct-2017
URI: http://hdl.handle.net/10044/1/52825
DOI: https://dx.do.org/10.1109/TPAMI.2017.2784421
ISSN: 0162-8828
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2668
End Page: 2681
Journal / Book Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 40
Issue: 11
Copyright Statement: © 2017 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/Funder: Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (E
Funder's Grant Number: EP/J017787/1
EP/N007743/1
Keywords: Science & Technology
Technology
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
Computer Science
Engineering
Low-rank
sparsity
facial expression synthesis
face age progression
joint and individual components
SYSTEMS
FACES
SETS
0801 Artificial Intelligence And Image Processing
0806 Information Systems
0906 Electrical And Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status: Published
Online Publication Date: 2017-12-18
Appears in Collections:Faculty of Engineering
Computing



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