Abstract
Face completion is a challenging task in computer vision. Unlike general images, face images usually have strong semantic correlation and symmetry. Without taking these characteristics into account, existing face completion techniques usually fail to produce a photo-realistic result, especially for the missing key components (e.g., eyes and mouths). In this paper, we propose a symmetry-aware face completion method based on facial structural features using a deep generative model. The model is trained with a combination of a reconstruction loss, a structure loss, two adversarial losses and a symmetry loss, which ensures pixel faithfulness, local-global contents integrity and symmetrical consistency. We conduct a dedicated symmetry detection technique for facial components and show that the symmetrical attention module significantly improves face completion results. Experiments show that our method is capable of synthesizing semantically valid and visually plausible contents for the missing facial key parts from random mask. In addition, our model outperforms other methods for detail completion of facial components.
This work was supported by Tianjin Philosophy and Social Science Planning Program under grant TJSR15-008.
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05 October 2021
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References
Afifi, M., Hussain, K.F.: MPB: a modified poisson blending technique. Comput. Vis. Media 1(4), 331–341 (2015)
Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. (ToG) 28(3), 24 (2009)
Bertalmio, M., Sapiro, G., Caselles, V., Ballester, C.: Image inpainting. In: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 417–424. ACM Press/Addison-Wesley Publishing Co. (2000)
Bitouk, D., Kumar, N., Dhillon, S., Belhumeur, P., Nayar, S.K.: Face swapping: automatically replacing faces in photographs. ACM Trans. Graph. (TOG) 27, 39 (2008)
Deng, Y., Dai, Q., Zhang, Z.: Graph Laplace for occluded face completion and recognition. IEEE Trans. Image Process. 20(8), 2329–2338 (2011)
Deng, Y., Li, D., Xie, X., Lam, K.M., Dai, Q.: Partially occluded face completion and recognition. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 4145–4148. IEEE (2009)
Dolhansky, B., Ferrer, C.C.: Eye in-painting with exemplar generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7902–7911 (2018)
Elad, M., Starck, J.L., Querre, P., Donoho, D.L.: Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA). Appl. Comput. Harmonic Anal. 19(3), 340–358 (2005)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Hays, J., Efros, A.A.: Scene completion using millions of photographs. ACM Trans. Graph. (TOG) 26, 4 (2007)
Iizuka, S., Simo-Serra, E., Ishikawa, H.: Globally and locally consistent image completion. ACM Trans. Graph. (TOG) 36(4), 107 (2017)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S.: Mask-specific inpainting with deep neural networks. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 523–534. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11752-2_43
Li, Y., Liu, S., Yang, J., Yang, M.H.: Generative face completion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 3 (2017)
Liu, G., Reda, F.A., Shih, K.J., Wang, T.C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. arXiv preprint arXiv:1804.07723 (2018)
Liu, P., Qi, X., He, P., Li, Y., Lyu, M.R., King, I.: Semantically consistent image completion with fine-grained details. arXiv preprint arXiv:1711.09345 (2017)
Mirza, M., Osindero, S.: Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)
Mo, Z., Lewis, J.P., Neumann, U.: Face inpainting with local linear representations. In: BMVC, vol. 1, p. 2 (2004)
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536–2544 (2016)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Ren, J.S., Xu, L., Yan, Q., Sun, W.: Shepard convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 901–909 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Saito, Y., Kenmochi, Y., Kotani, K.: Estimation of eyeglassless facial images using principal component analysis. In: Proceedings of the 1999 International Conference on Image Processing, ICIP 1999, vol. 4, pp. 197–201. IEEE (1999)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001, vol. 1, p. I. IEEE (2001)
Xie, J., Xu, L., Chen, E.: Image denoising and inpainting with deep neural networks. In: Advances in Neural Information Processing Systems, pp. 341–349 (2012)
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., Li, H.: High-resolution image inpainting using multi-scale neural patch synthesis. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, p. 3 (2017)
Yeh, R.A., Chen, C., Lim, T.Y., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: Semantic image inpainting with deep generative models. In: CVPR, vol. 2, p. 4 (2017)
Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Generative image inpainting with contextual attention. arXiv preprint (2018)
Zhang, S., He, R., Sun, Z., Tan, T.: Multi-task convnet for blind face inpainting with application to face verification. In: 2016 International Conference on Biometrics (ICB), pp. 1–8. IEEE (2016)
Zhang, S., He, R., Sun, Z., Tan, T.: DeMeshNet: blind face inpainting for deep MeshFace verification. IEEE Trans. Inf. Forensics Secur. 13(3), 637–647 (2018)
Zhang, W., Shan, S., Chen, X., Gao, W.: Local Gabor binary patterns based on Kullback–Leibler divergence for partially occluded face recognition. IEEE Signal Process. Lett. 14(11), 875–878 (2007)
Zhuang, Y.T., Wang, Y.S., Shih, T.K., Tang, N.C.: Patch-guided facial image inpainting by shape propagation. J. Zhejiang Univ.-SCIENCE A 10(2), 232–238 (2009)
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Zhang, J., Zhan, R., Sun, D., Pan, G. (2019). Symmetry-Aware Face Completion with Generative Adversarial Networks. In: Jawahar, C., Li, H., Mori, G., Schindler, K. (eds) Computer Vision – ACCV 2018. ACCV 2018. Lecture Notes in Computer Science(), vol 11364. Springer, Cham. https://doi.org/10.1007/978-3-030-20870-7_18
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