Applying Aging Effect on Facial Image with Multi-domain Generative Adversarial Network

Applying Aging Effect on Facial Image with Multi-domain Generative Adversarial Network

Автор: Shuvendu Roy

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 12 vol.11, 2019 года.

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Face Aging is an important and challenging application in computer vision. This is an application of conditional image generation. Until recently generative model was not good enough to generate considerable good resolution images. A generative model called generative adversarial network has introduced impressive capabilities in generating realistic images in both unconditional and conditional settings. Still, the task of generating images of different age conditioning on a given image is a very challenging task. Because there are two constraints to satisfy here in the generated images. The generated image must preserve the identity of the person in the source image and the image must have the features of the target age. In this work, we have applied the generative adversarial network in conditional settings along with custom loss function to satisfy the two mentioned constraints. The experiment has shown improved performance both in preserving the person’s identity and classification accuracy of generated images in the target class compared to previous known approach to this problem.

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Face-Aging, GAN, CNN, Generative-Model, Face-Synthesis

Короткий адрес: https://sciup.org/15017054

IDR: 15017054   |   DOI: 10.5815/ijigsp.2019.12.02

Список литературы Applying Aging Effect on Facial Image with Multi-domain Generative Adversarial Network

  • Y. Fu, G. Guo, and T. S. Huang, “Age synthesis and estimation via faces: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 32, no. 11, pp. 1955–1976, 2010.
  • X. Shu, J. Tang, H. Lai, L. Liu, and S. Yan, “Personalized age progression with aging dictionary,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3970–3978.
  • B. Tiddeman, M. Burt, and D. Perrett, “Prototyping and transforming facial textures for perception research,” IEEE computer graphics and applications, vol. 21, no. 5, pp. 42–50, 2001.
  • I. Kemelmacher-Shlizerman, S. Suwajanakorn, and S. M. Seitz, “Illumination-aware age progression,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 3334–3341.
  • J. Suo, S.-C. Zhu, S. Shan, and X. Chen, “A compositional and dynamic model for face aging,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 3, pp. 385–401, 2010.
  • Y. Tazoe, H. Gohara, A. Maejima, and S. Morishima, “Facial aging simulator considering geometry and patch-tiled texture,” in ACM SIGGRAPH 2012 Posters. ACM, 2012, p. 90.
  • I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems, 2014, pp. 2672–2680.
  • D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013.
  • M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv preprint arXiv:1411.1784, 2014.
  • P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” CVPR, 2017.
  • J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” 2017.
  • A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” arXiv preprint arXiv:1610.09585, 2016.
  • Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” arXiv preprint, vol. 1711, 2017.
  • C. Ledig, L. Theis, F. Husza´r, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang et al., “Photo-realistic single image super-resolution using a generative adversarial network.” in CVPR, vol. 2, no. 3, 2017, p. 4.
  • K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
  • O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al., “Imagenet large scale visual recognition challenge,” International Journal of Computer Vision, vol. 115, no. 3, pp. 211–252, 2015.
  • A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015.
  • S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015.
  • G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science, vol. 313, no. 5786, pp. 504–507, 2006.
  • O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention. Springer, 2015, pp. 234–241.
  • K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  • I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville, “Improved training of wasserstein gans,” in Advances in Neural Information Processing Systems, 2017, pp. 5767–5777.
  • M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv preprint arXiv:1701.07875, 2017.
  • A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differ- entiation in pytorch,” 2017.
  • S. Y. Zhang, Zhifei and H. Qi, “Age progression/regression by conditional adversarial autoencoder,” in IEEE Conference on Com- puter Vision and Pattern Recognition (CVPR). IEEE, 2017.
  • D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
  • G. Antipov, M. Baccouche, and J.-L. Dugelay, “Face aging with conditional generative adversarial networks,” in Image Processing (ICIP), 2017 IEEE International Conference on. IEEE, 2017, pp. 2089– 2093.
  • B. Amos, B. Ludwiczuk, and M. Satyanarayanan, “Openface: A general-purpose face recognition library with mobile applications,” CMU-CS-16-118, CMU School of Computer Science, Tech. Rep., 2016.
  • G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks.” in CVPR, vol. 1, no. 2, 2017, p. 3.
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