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An Improved Technique for Face Age Progression and Enhanced Super-Resolution with Generative Adversarial Networks

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Abstract

Several techniques are available for face age progression still identity preservation as well age estimation accuracy are big challenges and need attention. So, the proposed work is focused on these key issues using Generative Adversarial Networks (GANs). To produce a realistic appearance with an enhanced vision of face image, a fusion-based Generative Adversarial Network approach is used. GAN has a generator and a discriminator network. The generator produces fake images which are further differentiated by discriminator whether the image is real or fake. Initially, Cycle-Generative Adversarial Network (CycleGAN) achieves the face age progression, further Enhanced Super-resolution Generative Adversarial Network (ESRGAN) automatically enhance the aged face image to improve the visibility. Simulation results on five face datasets, namely IMDB-WIKI, CACD and UTKFace, FGNET, Celeb A are evaluated. The proposed work efficacy is observed in comparison to previous techniques using a quantitative Face ++ research toolkit with parameters confidence score number and age estimation value. It is observed that the proposed work produces the aged face precisely with an error rate of 0.001%, with a a confidence score 95.13 to 95.39 on datasets.

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References

  1. Hernandez, J. (2015). The boy who didn’t know he was abducted for thirteen years. https://www.washingtonpost.com/news/morning-mix/wp/2015/11/05/.

  2. Fu, Y., Guo, G., & Huang, T. S. (2010). Age synthesis and estimation via faces: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(11), 1955–1976.

    Article  Google Scholar 

  3. Jain, A. K., Dass, S. C., & Nandakumar, K., (2006). Soft biometric traits for personal recognition systems. In Proceeding. international conference visualization, imaging, and image processing (pp. 249–253).

  4. Georgopoulos, M., Panagakis, Y., & Pantic, M., (2018). Modeling of facial aging and kinship: A survey. In Computer vision and pattern recognition (pp. 1–25). arXiv:180204636v1.

  5. Deb, D., Aggarwal, D., Jain, A. K. (2020). Child face age-progression via deep feature aging. In Computer vision and pattern recognition (pp. 1–25). arXiv:2003.08788.

  6. Yang, H., Huang, D., Wang, Y., & Jain, A. K. (2018). Learning face age progression: A Pyramid Architecture of GANs. In IEEE.

  7. Antipov, G., Baccouche, M., & Dugelay, J. L. (2017). Face aging with conditional generative adversarial networks. In Computer vision and pattern recognition. arXiv:1702.01983v1.

  8. Liu, Y., Li, Q., & Sun, Z. (2019). A3GAN: An attribute-aware attentive generative adversarial network for face aging. In Computer vision and pattern recognition (pp. 1–16). arXiv:1911.06531.

  9. Bessinger, Z., & Jacobs, N. (2019). A generative model of worldwide facial appearance. In IEEE winter conference on applications of computer vision (WACV).

  10. Rew, J., Choi, Y. H., Kim, H., & Hwang, E. (2019). Skin aging estimation scheme based on lifestyle and dermoscopy image analysis. Applied Science, 9(6), 1228.

    Article  Google Scholar 

  11. Shooshtari, S., Menec, V., Swift, A., & Tate, R. (2020). Exploring ethno-cultural variations in how older Canadians define healthy aging: The Canadian Longitudinal Study on Aging (CLSA). Journal of Aging Studies 52.

  12. Suo, J., Zhu, S., Shan, S., & Chen, X. (2010). A compositional and dynamic model for face aging. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(3), 285–401.

    Google Scholar 

  13. Yang, H., Huang, D., Wang, Y., Wang, H., & Tang, Y. (2016). Face aging effect simulation using hidden factor analysis joint sparse representation. IEEE Transactions on Image Processing, 25(6), 2493–2507.

    Article  MathSciNet  Google Scholar 

  14. Kemelmacher-Shilzeman, I., Suwajanakorn, S., & Seitz, S. M. (2014). Illumination-aware age progression. In Proceeding IEEE international conference computer vision and pattern recognition (pp. 3334–3341).

  15. Karras, T., Samuli Laine, T. A., & Lehtinen, J., (2018). Progressive growing of GAN for improved quality, Stability and variation. In Conference ICLR, arXiv:1710.10196v3.

  16. Radford, A., Metz, L. & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434v2.

  17. Zhao, J., Mathieu, M., & Lecun, Y. (2017). Energy-based generative adversarial networks. In International conference on learning representation (ICLR).

  18. Xu, C., Makihara, Y., Yagi, Y., & Lu, J. (2019). Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification. In Machine vision and applications (pp. 629–644).

  19. Luan, F., Paris, S., Shechtman, E., & Bala, K. (2017). Deep photo style transfer. arXiv:1703.07511v3.

  20. Khan, K., Attique, M., Syed, I., & Gul, A. (2019). Automatic gender classification through face segmentation. Symmetry, 11, 770.

    Article  Google Scholar 

  21. Tolosana, R., Vera-Rodriguez, R., Fierrez, J., Morales, A., & Ortega-Garcia, J. (2020) Deep fakes and beyond: A survey of face manipulation and fake detection. In Computer vision and pattern recognition (pp. 1–15). arXiv:2001.00179.

  22. Uricar, M., Krizek, P., Hurych D., Sobh, I., Yogamani, S., & Denny, P. (2020). Yes, we GAN: Applying adversarial techniques for autonomous driving. In Computer vision and pattern recognition (pp 1–16). arXiv:1902.03442v2.

  23. Goodfellow, I. J., Pougetabadie, J., Mirza, M., et al. (2014). Generative adversarial network. Advance in Neural Information Processing Systems, 3, 2672–2680.

    Google Scholar 

  24. Creswellx, A., White, T., Dumoulinz, V. & Arulkumaranx, K., et al. (2017). Generative adversarial networks: An overview. arXiv:1710.07035v1.

  25. Chu, C., Minami, K., & Fukumizu, K. (2020). Smoothness and stability in GANs. In ICLR 2020 conference (pp. 1–31).

  26. Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. (2018). Unpaired image to image translation using cycle -consistent adversarial networks. arXiv:1703.10593v6.

  27. Li, C., & Wand, M. (2016). Precomputed real-time texture synthesis with Markovian generative adversarial networks. In European conference on computer vision (pp. 702–716). Springer.

  28. Huang, H., He, R., Sun, Z., & Tan, T., (2017). Wavelet-SRNET: A wavelet-based CNN for multi-scale face Super- resolution. In Proceeding IEEE international conference computer vision (pp. 1689–1697).

  29. Liu, M.-Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. In Proceeding advancement neural information process system (pp. 700–708).

  30. Liu, M.-Y., & Tuzel, O. (2016). Coupled generative adversarial networks. In Proceeding advancement neural information process system (pp. 469–477).

  31. Huang, H., He, R., Sun, Z., & Tan, T., et al. (2018). Introvae: Introspective variational autoencoders for photographic image synthesis. In Proceeding advancement neural information process system (pp. 52–63).

  32. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv:1701.07875.

  33. Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016). Generative adversarial text to image synthesis. In Proceeding international conference on machine learning (pp. 1060–1069).

  34. Isola, P., Zhu, J., & Efros, A. (2017). Image to image translation with conditional adversarial network. In Proceeding IEEE international conference computer vision and pattern recognition (pp. 1125–1134).

  35. Choi, Y., Choi, M., Kim, M., Ha, J., Kim, S. & Choo, J. (2018). StarGAN: Unified generative adversarial networks for multi-domain image to image translation. arXiv:1711.09020.

  36. Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X., Xu, T., et al. (2018). Attngan: Fine-grained text to image generation with attentional generative adversarial networks. Computer Vision and Pattern Recognition, 1711, 10485.

    Google Scholar 

  37. Karras, T., Laine, S., & Aila, T. (2019). A style-based generator architecture for generative adversarial networks. arXiv:1812.04948.

  38. Ledig, C., Theis, L., Huszar, F., & Caballero, J., et. al. (2017). Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802v5.

  39. Wang, X., Yu, K., Wu, S., & Gu, J., et. al. (2018). ESRGAN: Enhanced super-resolution generative adversarial networks. arXiv:1809.00219.

  40. Wang, C.-C., Liu, H.-H., Pei, S.-C., Liu, K.-H., & Liu, T.-J. (2019). Face aging on realistic photos by generative adversarial networks. In IEEE.

  41. Megvii Inc. (2013). Face ++ research toolkit. http://www.faceplusplus.com/.

  42. TLGAN:TransparentLatentSpaceGAN. https://github.com/SummitKwan/transparent_latent_gan.

  43. Liu, X., Zou, Y., Xie, C., Kuang, H., & Ma, X. (2019). Bidirectional face aging synthesis based on improved deep convolutional generative adversarial networks. Information, 10(2), 69.

    Article  Google Scholar 

  44. Zhang, Z., Song, Y., & Qi, H. (2017). Age progression/regression by conditional adversarial autoencoder. In Proceeding. IEEE international conference computer vision and pattern recognition (pp. 5810–5818).

  45. Aging Booth. PiVi & Co. (2016). https://itunes.apple.com/us/app/agingbooth/id357467791?mt=8.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Punjabi University, Patiala, Punjab, 147001, India

    Neha Sharma & Reecha Sharma

  2. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147001, India

    Neeru Jindal

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  1. Neha Sharma
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  2. Reecha Sharma
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  3. Neeru Jindal
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Correspondence to Neha Sharma.

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Sharma, N., Sharma, R. & Jindal, N. An Improved Technique for Face Age Progression and Enhanced Super-Resolution with Generative Adversarial Networks. Wireless Pers Commun 114, 2215–2233 (2020). https://doi.org/10.1007/s11277-020-07473-1

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