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Regular Paper

Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks

National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.

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Journal of Computer Science and Technology
Pages 511-521
Cite this article:
Li H-Y, Dong W-M, Hu B-G. Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks. Journal of Computer Science and Technology, 2018, 33(3): 511-521. https://doi.org/10.1007/s11390-018-1835-2

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Received: 25 December 2017
Revised: 20 March 2018
Published: 11 May 2018
©2018 LLC & Science Press, China
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