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Age-oriented face synthesis with conditional discriminator pool and adversarial triplet loss
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Wang, Haoyi, Sanchez Silva, Victor and Li, Chang-Tsun (2021) Age-oriented face synthesis with conditional discriminator pool and adversarial triplet loss. IEEE Transactions on Image Processing, 30 . pp. 5413-5425. doi:10.1109/TIP.2021.3084106 ISSN 1057-7149.
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Official URL: https://doi.org/10.1109/TIP.2021.3084106
Abstract
The vanilla Generative Adversarial Networks
(GANs) are commonly used to generate realistic images depicting
aged and rejuvenated faces. However, the performance of such
vanilla GANs in the age-oriented face synthesis task is often
compromised by the mode collapse issue, which may produce
poorly synthesized faces with indistinguishable visual variations.
In addition, recent age-oriented face synthesis methods use the
L1 or L2 constraint to preserve the identity information on
synthesized faces, which implicitly limits the identity permanence
capabilities when these constraints are associated with a trivial
weighting factor. In this paper, we propose a method for the ageoriented
face synthesis task that achieves high synthesis accuracy
with strong identity permanence capabilities. Specifically, to
achieve high synthesis accuracy, our method tackles the mode
collapse issue with a novel Conditional Discriminator Pool, which
consists of multiple discriminators, each targeting one particular
age category. To achieve strong identity permanence capabilities,
our method uses a novel Adversarial Triplet loss. This loss,
which is based on the Triplet loss, adds a ranking operation
to further pull the positive embedding towards the anchor
embedding to significantly reduce intra-class variances in the
feature space. Through extensive experiments, we show that our
proposed method outperforms state-of-the-art methods in terms
of synthesis accuracy and identity permanence capabilities, both
qualitatively and quantitatively.
Item Type: | Journal Article | ||||||
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Alternative Title: | |||||||
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
Library of Congress Subject Headings (LCSH): | Human face recognition (Computer science) , Optical pattern recognition, Visual discrimination , Face perception -- Computer simulation | ||||||
Journal or Publication Title: | IEEE Transactions on Image Processing | ||||||
Publisher: | IEEE | ||||||
ISSN: | 1057-7149 | ||||||
Official Date: | 2 June 2021 | ||||||
Dates: |
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Volume: | 30 | ||||||
Page Range: | pp. 5413-5425 | ||||||
DOI: | 10.1109/TIP.2021.3084106 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Reuse Statement (publisher, data, author rights): | © 2021 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. | ||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||
Copyright Holders: | IEEE | ||||||
Date of first compliant deposit: | 14 June 2021 | ||||||
Date of first compliant Open Access: | 14 June 2021 | ||||||
RIOXX Funder/Project Grant: |
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