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Facial Expression Restoration Based on Improved Graph Convolutional Networks

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MultiMedia Modeling (MMM 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11962))

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Abstract

Facial expression analysis in the wild is challenging when the facial image is with low resolution or partial occlusion. Considering the correlations among different facial local regions under different facial expressions, this paper proposes a novel facial expression restoration method based on generative adversarial network by integrating an improved graph convolutional network (IGCN) and region relation modeling block (RRMB). Unlike conventional graph convolutional networks taking vectors as input features, IGCN can use tensors of face patches as inputs. It is better to retain the structure information of face patches. The proposed RRMB is designed to address facial generative tasks including inpainting and super-resolution with facial action units detection, which aims to restore facial expression as the ground-truth. Extensive experiments conducted on BP4D and DISFA benchmarks demonstrate the effectiveness of our proposed method through quantitative and qualitative evaluations.

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References

  1. Chen, Y., Tai, Y., Liu, X., Shen, C., Yang, J.: FSRNet: end-to-end learning face super-resolution with facial priors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2492–2501 (2018)

    Google Scholar 

  2. Cheon, M., Kim, J.-H., Choi, J.-H., Lee, J.-S.: Generative adversarial network-based image super-resolution using perceptual content losses. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11133, pp. 51–62. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11021-5_4

    Chapter  Google Scholar 

  3. Ekman, R.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, Oxford (1997)

    Google Scholar 

  4. Engin, D., Genç, A., Kemal Ekenel, H.: Cycle-Dehaze: enhanced cyclegan for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 825–833 (2018)

    Google Scholar 

  5. Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1263–1272. JMLR. org (2017)

    Google Scholar 

  6. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  7. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  8. Huang, R., Zhang, S., Li, T., He, R.: Beyond face rotation: Global and local perception GAN for photorealistic and identity preserving frontal view synthesis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2439–2448 (2017)

    Google Scholar 

  9. Khorrami, P., Paine, T., Huang, T.: Do deep neural networks learn facial action units when doing expression recognition? In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 19–27 (2015)

    Google Scholar 

  10. Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)

  11. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  12. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  13. Li, L., Pan, J., Lai, W.S., Gao, C., Sang, N., Yang, M.H.: Learning a discriminative prior for blind image deblurring. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6616–6625 (2018)

    Google Scholar 

  14. Li, W., Abtahi, F., Zhu, Z., Yin, L.: EAC-Net: a region-based deep enhancing and cropping approach for facial action unit detection. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 103–110. IEEE (2017)

    Google Scholar 

  15. Li, Y., Liu, S., Yang, J., Yang, M.H.: Generative face completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3911–3919 (2017)

    Google Scholar 

  16. Mavadati, S.M., Mahoor, M.H., Bartlett, K., Trinh, P., Cohn, J.F.: DISFA: a spontaneous facial action intensity database. IEEE Trans. Affect. Comput. 4(2), 151–160 (2013)

    Article  Google Scholar 

  17. Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)

  18. Muhammad, N., Bibi, N., Jahangir, A., Mahmood, Z.: Image denoising with norm weighted fusion estimators. Pattern Anal. Appl. 21(4), 1013–1022 (2018)

    Article  MathSciNet  Google Scholar 

  19. Peng, G., Wang, S.: Weakly supervised facial action unit recognition through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2188–2196 (2018)

    Google Scholar 

  20. Shao, Z., Liu, Z., Cai, J., Ma, L.: Deep adaptive attention for joint facial action unit detection and face alignment. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 725–740. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_43

    Chapter  Google Scholar 

  21. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  22. Song, Y., et al.: Joint face Hallucination and deblurring via structure generation and detail enhancement. Int. J. Comput. Vis. 127, 1–16 (2018)

    Google Scholar 

  23. Taheri, S., Qiu, Q., Chellappa, R.: Structure-preserving sparse decomposition for facial expression analysis. IEEE Trans. Image Process. 23(8), 3590–3603 (2014)

    Article  MathSciNet  Google Scholar 

  24. Wang, J., Li, X., Yang, J.: Stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1788–1797 (2018)

    Google Scholar 

  25. Wang, Y.T., Zhao, X.L., Jiang, T.X., Deng, L.J., Chang, Y., Huang, T.Z.: Rain streak removal for single image via kernel guided CNN. arXiv preprint arXiv:1808.08545 (2018)

  26. Wu, Y., Ji, Q.: Constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3400–3408 (2016)

    Google Scholar 

  27. Yeh, R.A., Chen, C., Yian Lim, T., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5485–5493 (2017)

    Google Scholar 

  28. Zhang, X., et al.: A high-resolution spontaneous 3D dynamic facial expression database. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–6. IEEE (2013)

    Google Scholar 

  29. Zhao, K., Chu, W.S., De la Torre, F., Cohn, J.F., Zhang, H.: Joint patch and multi-label learning for facial action unit detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2207–2216 (2015)

    Google Scholar 

  30. Zhao, K., Chu, W.S., Zhang, H.: Deep region and multi-label learning for facial action unit detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3391–3399 (2016)

    Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grants of 41806116 and 61503277. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research.

Author information

Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Zhilei Liu, Le Li & Yunpeng Wu

  2. School of Marine Science and Technology, Tianjin University, Tianjin, China

    Cuicui Zhang

Authors
  1. Zhilei Liu
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  2. Le Li
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  3. Yunpeng Wu
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  4. Cuicui Zhang
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Corresponding author

Correspondence to Cuicui Zhang .

Editor information

Editors and Affiliations

  1. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Yong Man Ro

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Huang Cheng

  3. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Junmo Kim

  4. National Cheng Kung University, Tainan City, Taiwan

    Wei-Ta Chu

  5. Tsinghua University, Beijing, China

    Peng Cui

  6. Korea Advanced Institute of Science and Technology, Daejeon, Korea (Republic of)

    Jung-Woo Choi

  7. National Tsing Hua University, Hsinchu, Taiwan

    Min-Chun Hu

  8. Ghent University, Ghent, Belgium

    Wesley De Neve

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Liu, Z., Li, L., Wu, Y., Zhang, C. (2020). Facial Expression Restoration Based on Improved Graph Convolutional Networks. In: Ro, Y., et al. MultiMedia Modeling. MMM 2020. Lecture Notes in Computer Science(), vol 11962. Springer, Cham. https://doi.org/10.1007/978-3-030-37734-2_43

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  • DOI: https://doi.org/10.1007/978-3-030-37734-2_43

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37733-5

  • Online ISBN: 978-3-030-37734-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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