? Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (3) :511-521    DOI: 10.1007/s11390-018-1835-2
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Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks
Huai-Yu Li1,2, Wei-Ming Dong1,*, Member, CCF, ACM, IEEE, Bao-Gang Hu1, Senior Member, IEEE, Member, CCF
1 National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;
2 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 should be similar with 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 method can learn and generate high-quality identity-preserving facial images with specified attributes.
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KeywordsGenerative Adversarial Networks   Image Editing   Facial Attributes Transformation     
Received 2017-12-25;
Fund:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 61672520, 61573348, 61620106003, and 61720106006, the Beijing Natural Science Foundation of China under Grant No. 4162056, the National Key Technology Research and Development Program of China under Grant No. 2015BAH53F02, and the CASIA-Tencent YouTu Jointly Research Project. The Titan X used for this research was donated by the NVIDIA Corporation.

Corresponding Authors: Wei-Ming Dong     Email: weiming.dong@ia.ac.cn
About author: Huai-Yu Li is a Ph.D. student in Institute of Automation, Chinese Academy of Sciences, Beijing, under the supervision of Prof. Bao-Gang Hu. He earned his Bachelor's degree in electronic information engineering from Northeast University, Shenyang, in 2014. His research interests are in artificial intelligence, computer vision, and deep learning.
Cite this article:   
Huai-Yu Li, Wei-Ming Dong, Bao-Gang Hu.Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks[J]  Journal of Computer Science and Technology, 2018,V33(3): 511-521
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