KINSHIPGAN: SYNTHESIZING OF KINSHIP FACES FROM FAMILY PHOTOS BY REGULARIZING A DEEP FACE NETWORK

Date

2018-10-10

Author

Ozkan, Savas
Ozkan, Akin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Kinship Synthesis; ; , Generative Adversarial Network, Fully Convolutional Networks

URI

https://hdl.handle.net/11511/64705

Conference Name

25th IEEE International Conference on Image Processing (ICIP)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

S. Ozkan and A. Ozkan, “KINSHIPGAN: SYNTHESIZING OF KINSHIP FACES FROM FAMILY PHOTOS BY REGULARIZING A DEEP FACE NETWORK,” Athens, GREECE, 2018, p. 2142, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/64705.