Kinshipgan: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network

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Date

2018

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Volume Title

Publisher

IEEE Computer Society

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Green Open Access

Yes

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Top 10%
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Top 10%

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Abstract

In this paper, we propose a kinship generator network that can synthesize a possible child face by analyzing his/her parent's photo. For this purpose, we focus on to handle the scarcity of kinship datasets throughout the paper by proposing novel solutions in particular. To extract robust features, we integrate a pre-trained face model to the kinship face generator. Moreover, the generator network is regularized with an additional face dataset and adversarial loss to decrease the overfitting of the limited samples. Lastly, we adapt cycle-domain transformation to attain a more stable results. Experiments are conducted on Families in the Wild (FIW) dataset. The experimental results show that the contributions presented in the paper provide important performance improvements compared to the baseline architecture and our proposed method yields promising perceptual results. © 2018 IEEE.

Description

The Institute of Electrical and Electronics Engineers Signal Processing Society

Keywords

Fully Convolutional Networks, Generative Adversarial Network, Kinship Synthesis, FOS: Computer and information sciences, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition

Fields of Science

03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Citation

WoS Q

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Q3
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OpenCitations Citation Count
19

Source

Proceedings - International Conference on Image Processing, ICIP -- 25th IEEE International Conference on Image Processing, ICIP 2018 -- 7 October 2018 through 10 October 2018 -- Athens -- 143052

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2142

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2146

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CrossRef : 7

Scopus : 21

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22

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4

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