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PFA-GAN: Pose Face Augmentation Based on Generative Adversarial Network
Volume 32, Issue 2 (2021), pp. 425–440
Pub. online: 29 January 2021      Type: Research Article      Open accessOpen Access
Received
1 June 2020
Accepted
1 January 2021
Published
29 January 2021

Abstract

In this work, we propose a novel framework based on Generative Adversarial Networks for pose face augmentation (PFA-GAN). It enables a controlled pose synthesis of a new face image from a source face given a driving one while preserving the identity of the source face. We introduce a method for training the framework in a fully self-supervised mode using a large-scale dataset of unconstrained face images. Besides, some augmentation strategies are presented to expand the training set. The face verification experimental results demonstrate the effectiveness of the presented augmentation strategies as all augmented datasets outperform the baseline.

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Copyright
© 2021 Vilnius University
Open access article under the CC BY license.

Keywords
generative adversarial networks face verification visual data augmentation

Funding
This work was financially supported by the Government of the Russian Federation (Grant 08-08).

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