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

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Date

2018

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IEEE Computer Society

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Department of Electrical & Electronics Engineering
Department of Electrical and Electronics Engineering (EE) offers solid graduate education and research program. Our Department is known for its student-centered and practice-oriented education. We are devoted to provide an exceptional educational experience to our students and prepare them for the highest personal and professional accomplishments. The advanced teaching and research laboratories are designed to educate the future workforce and meet the challenges of current technologies. The faculty's research activities are high voltage, electrical machinery, power systems, signal and image processing and photonics. Our students have exciting opportunities to participate in our department's research projects as well as in various activities sponsored by TUBİTAK, and other professional societies. European Remote Radio Laboratory project, which provides internet-access to our laboratories, has been accomplished under the leadership of our department with contributions from several European institutions.

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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

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Citation

19

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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

Volume

Issue

Start Page

2142

End Page

2146

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