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Dual reference age synthesis

Lookup NU author(s): Dr Bingzhang Hu, Dr Yu Guan

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Abstract

© 2020 Elsevier B.V.Age synthesis methods typically take a single image as input and use a specific number to control the age of the generated image. In this paper, we propose a novel framework taking two images as inputs, named dual-reference age synthesis (DRAS), which approaches the task differently; instead of using “hard” age information, i.e. a fixed number, our model determines the target age in a “soft” way, by employing a second reference image. Specifically, the proposed framework consists of an identity agent, an age agent and a generative adversarial network. It takes two images as input – an identity reference and an age reference – and outputs a new image that shares corresponding features with each. Experimental results on two benchmark datasets (UTKFace and CACD) demonstrate the appealing performance and flexibility of the proposed framework.


Publication metadata

Author(s): Zhou Y, Hu B, He J, Guan Y, Shao L

Publication type: Article

Publication status: Published

Journal: Neurocomputing

Year: 2020

Volume: 411

Pages: 164-177

Print publication date: 21/10/2020

Online publication date: 16/06/2020

Acceptance date: 08/06/2020

ISSN (print): 0925-2312

ISSN (electronic): 1872-8286

Publisher: Elsevier B.V.

URL: https://doi.org/10.1016/j.neucom.2020.06.023

DOI: 10.1016/j.neucom.2020.06.023


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