A Facial Expression Synthesis Method Based on Generative Adversarial Network
Authors: 
Bin Xu, Weiran Li, Qing ZhuAuthors Info & Claims
Pages 677 - 681
Abstract
Recently, machine learning, especially the emergence of generative adversarial networks (GANs), has further enhanced the robustness and realism of facial expression conversion models. However, most models have flaws such as fuzziness in the details. Based on this, this article mainly studies the facial expression synthesis method based on GANs. Firstly, we created a dataset containing 127,616 expression annotations suitable for the study of facial expressions. The dataset has been tested on mainstream models with good generation results. Secondly, we propose a GAN network structure named SRFEGAN with a super-resolution synthesis module. This module helps solve the artifact problem in the process of image conversion. Experimental results on our dataset show that the average recognition accuracy rate of the generated images is 63.76% and the Frechet Inception distance (FID) is 36.581. This shows that our network can accurately synthesize the facial expression image of the subject, and the image quality is better.
References
[1]
Noh, J. Y., & Neumann, U. . 1972. A survey of facial modeling and animation techniques.
[2]
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., & Dong, C., 2018. Esrgan: enhanced super-resolution generative adversarial networks. Springer, Cham. https://arxiv.org/abs/1809.00219
[3]
Goodfellow I, Pouget-Abadie J, Mirza M, 2014. Generative adversarial nets. Advances in neural information processing systems, 27.
[4]
Perarnau, G., Van De Weijer, J., Raducanu, B., & Álvarez, J. M. 2016. Invertible conditional gans for image editing. arXiv preprint arXiv:1611.06355.
[5]
Huang, Y., & Khan, S. M. 2017. Dyadgan: Generating facial expressions in dyadic interactions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 11-18).
[6]
Zhu, J. Y., Park, T., Isola, P., & Efros, A. A. . 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. IEEE. https://arxiv.org/abs/1703.10593
[7]
Ding, H., Sricharan, K., & Chellappa, R. 2018. Exprgan: Facial expression editing with controllable expression intensity. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
[8]
Choi, Y., Choi, M., Kim, M., Ha, J. W., & Choo, J.2018. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/1711.09020v3
[9]
Pumarola, A., Agudo, A., Martinez, A. M., Sanfeliu, A., & F Moreno-Noguer. 2018. GANimation: Anatomically-aware Facial Animation from a Single Image. European Conference on Computer Vision. Springer, Cham. https://arxiv.org/abs/1807.09251
[10]
Ekman, P., & Friesen, W. V. 1978. Facial action coding system: a technique for the measurement of facial movement. a technique for the measurement of facial action.
[11]
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. . 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Proceedings of the 2017 International Conference on Neural Information Processing Systems. Long Beach:Neural Information Processing Systems Foundation, 2017:6629-6640
[12]
Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A.2010. Presentation and validation of the radboud faces database. Cognition and emotion24(8), 1377–1388
[13]
Benitez-Quiroz, C. F., Srinivasan, R., & Martinez, A. M. . 2016. EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
[14]
Liu, Z., Ping, L., Wang, X., & Tang, X. . 2016. Deep learning face attributes in the wild. IEEE International Conference on Computer Vision
[15]
Cheong, J. H., Xie, T., Byrne, S., & Chang, L. J. . 2021. Py-feat: python facial expression analysis toolbox. https://arxiv.org/abs/2104.03509v1
[16]
Ledig C., Theis L., Huszár F., . 2017. 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).
[17]
Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., & Dong, C., 2018. Esrgan: enhanced super-resolution generative adversarial networks. Springer, Cham. https://arxiv.org/abs/1809.00219
[18]
Choi Y, Uh Y, Yoo J, 2020. StarGAN v2: Diverse Image Synthesis for Multiple Domains. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19]
Tang, H., Xu, D., Sebe, N., & Yan, Y. . 2019. Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation. 2019 International Joint Conference on Neural Networks (IJCNN). IEEE.https://arxiv.org/abs/1903.12296
- A Facial Expression Synthesis Method Based on Generative Adversarial Network
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Published In
March 2022
809 pages
ISBN:9781450396110
DOI:10.1145/3532213
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Published: 13 July 2022
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ICCAI '22
ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence
March 18 - 21, 2022
Tianjin, China
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