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Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks

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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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References

  1. Selim A, Elgharib M, Doyle L. Painting style transfer for head portraits using convolutional neural networks. ACM Trans. Graph., July 2016, 35(4): 129:1–129:18.

  2. Kemelmacher-Shlizerman I. Transfiguring portraits. ACM Trans. Graph., 2016, 35(4): 94:1–94:8.

    Article  Google Scholar 

  3. Li C, Zhou K, Lin S. Simulating makeup through physics-based manipulation of intrinsic image layers. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2015, pp.4621–4629.

  4. Liu S, Ou X, Qian R, Wang W, Cao X. Makeup like a superstar: Deep localized makeup transfer network. In Proc. the 25th International Joint Conference on Artificial Intelligence, July 2016, pp.2568–2575.

  5. Tran L, Yin X, Liu X. Disentangled representation learning gan for pose-invariant face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2017, pp.1283–1292.

  6. Chen C, Dantcheva A, Ross A. Automatic facial makeup detection with application in face recognition. In Proc. International Conference on Biometrics, February 2013.

  7. Yao N M, Chen H, Guo Q P, Wang H A. Non-frontal facial expression recognition using a depth-patch based deep neural network. Journal of Computer Science and Technology, 2017, 32(6): 1172–1185.

    Article  Google Scholar 

  8. Mohammed U, Prince S, Kautz J. Visio-lization: Generating novel facial images. ACM Trans. Graph., 2009, 28(3): 57:1–57:8.

    Article  Google Scholar 

  9. Salimans T, Karpathy A, Chen X, Kingma D P. Pixel-CNN++: Improving the Pixel-CNN with discretized logistic mixture likelihood and other modifications. In Proc. the 5th Int. Conf. Learning Representations, April 2017.

  10. Gregor K, Danihelka I, Graves A, Rezende D, Wierstra D. DRAW: A recurrent neural network for image generation. In Proc. the 32nd International Conference on Machine Learning, July 2015, pp.1462–1471.

  11. Kingma D P, Welling M. Auto-encoding variational bayes. In Proc. the 2nd Int. Conf. Learning Representations, April 2014.

  12. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In Proc. the 27th Advances in Neural Information Processing Systems, December 2014, pp.2672–2680.

  13. Upchurch P, Gardner J, Bala K, Pless R, Snavely N, Weinberger K. Deep feature interpolation for image content changes. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, July 2016, pp.6090–6099.

  14. Perarnau G, Weijer J, Raducanu B, Álvarez J M. Invertible conditional gans for image editing. In Proc. NIPS Workshop on Adversarial Training, December 2016.

  15. Yin W, Fu Y, Sigal L, Xue X. Semi-Latent GAN: Learning to generate and modify facial images from attributes. arXiv:1704.02166, 2017. https://arxiv.org/abs/1704.02166, April 2017.

  16. Liao J, Yao Y, Yuan L, Hua G, Kang S B. Visual attribute transfer through deep image analogy. ACM Trans. Graph., 2017, 36(4): 120:1–120:15.

  17. Lu Y, Tai Y W, Tang C K. Conditional CycleGAN for attribute guided face image generation. arXiv: 1705.09966, 2017. https://arxiv.org/abs/1705.09966, May 2017.

  18. Zhu J Y, Park T, Isola P, Efros A A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proc. International Conference on Computer Vision, Oct. 2017, pp.2242–2251.

  19. Choi Y, Choi M, Kim M, Ha J W, Kim S, Choo J. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. arXiv:1711.09020, 2017. https://arxiv.org/abs/1711.09020, November 2017.

  20. Mirza M, Osindero S. Conditional generative adversarial nets. arXiv: 1411.1784, 2014. https://arxiv.org/abs/1411.1784, Mar. 2018.

  21. Odena A, Olah C, Shlens J. Conditional image synthesis with auxiliary classifier GANs. In Proc. the 34th International Conference on Machine Learning, August 2017, pp.2642–2651.

  22. Isola P, Zhu J Y, Zhou T, Efros A A. Image-to-image translation with conditional adversarial networks. In Proc. Conference on Computer Vision and Pattern Recognition, July 2017, pp.5967–5976.

  23. Taigman Y, Polyak A, Wolf L. Unsupervised cross-domain image generation. In Proc. the 5th Int. Conf. Learning Representations, April 2017.

  24. Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In Proc. International Conference on Computer Vision, Dec. 2015, pp.3730–3738.

  25. Ronneberger O, Fischer P, Brox T et al. U-Net: Convolutional networks for biomedical image segmentation. In Proc. 18th Int. Conf. Medical Image Computing and Computer-Assisted Intervention, October 2015, pp.234–241.

  26. Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proc. the 32nd International Conference on Machine Learning, July 2015, pp.448–456.

  27. Maas A L, Hannun A Y, Ng A Y. Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML Workshop on Deep Learning for Audio, Speech and Language Processing, June 2013.

  28. Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In Proc. European Conference on Computer Vision, October 2014.

  29. Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado G S, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Jozefowicz R, Kaiser L, Kudlur M, Levenberg J, Mané D, Monga R, Moore S, Murray D, Olah C, Schuster M, Shlens J, Steiner B, Sutskever I, Talwar K, Tucker P, Vanhoucke V, Vasudevan V, Viégas F, Vinyals O, Warden P, Wattenberg M, Wicke M, Yu Y, Zheng X. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv: 1603.04467, https://arxiv.org/abs/1603.04467, Mar. 2018.

  30. Kingma D, Ba J. Adam: A method for stochastic optimization. In Proc. the 3rd Int. Conf. Learning Representations, May 2015.

  31. Yi Z, Zhang H, Tan P, Gong M. DualGAN: Unsupervised dual learning for image-to-image translation. In Proc. International Conference on Computer Vision, Oct. 2017, pp.2868–2876.

  32. Kim T, Cha M, Kim H, Lee J K, Kim J. Learning to discover cross-domain relations with generative adversarial networks. In Proc. the 34th International Conference on Machine Learning, August 2017, pp.1857–1865.

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Huai-Yu Li, Wei-Ming Dong & Bao-Gang Hu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Huai-Yu Li

Authors
  1. Huai-Yu Li
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  2. Wei-Ming Dong
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  3. Bao-Gang Hu
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Correspondence to Wei-Ming Dong.

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Li, HY., Dong, WM. & Hu, BG. Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks. J. Comput. Sci. Technol. 33, 511–521 (2018). https://doi.org/10.1007/s11390-018-1835-2

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  • DOI: https://doi.org/10.1007/s11390-018-1835-2

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