Learning Pixel Perception for Identity and Illumination Consistency Face Frontalization in the Wild
Author:
Affiliation:
1. College of Computer Science and Engineering, Shandong University of Science and Technology
2. State Key Laboratory of Virtual Reality and Technology, Beihang University
3. MiningLamp Technology
Publisher
Institute of Electronics, Information and Communications Engineers (IEICE)
Subject
Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://www.jstage.jst.go.jp/article/transinf/E106.D/5/E106.D_2022DLP0055/_pdf
Reference41 articles.
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2. [2] J. Deng, S. Cheng, N. Xue, Y. Zhou, and S. Zafeiriou, “UV-GAN: Adversarial facial uv map completion for pose-invariant face recognition,” IEEE Conference on Computer Vision and Pattern Recognition, pp.7093-7102, 2018. 10.1109/cvpr.2018.00741
3. [3] Y. Yin, S. Jiang, J.P. Robinson, and Y. Fu, “Dual-Attention GAN for Large-Pose Face Frontalization,” IEEE International Conference on Automatic Face and Gesture Recognition, pp.249-256, 2020. 10.1109/fg47880.2020.00004
4. [4] J. Zhao, Y. Cheng, Y. Xu, L. Xiong, J. Li, F. Zhao, K. Jayashree, S. Pranata, S. Shen, J. Xing, S. Yan, and J. Feng, “Towards pose invariant face recognition in the wild,” Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp.2207-2216, 2018. 10.1109/cvpr.2018.00235
5. [5] Y. Wei, M. Liu, H. Wang, R. Zhu, G. Hu, and W. Zuo, “Learning Flow-Based Feature Warping for Face Frontalization with Illumination Inconsistent Supervision,” Proc. European Conference on Computer Vision, vol.12357, pp.558-574, 2020. 10.1007/978-3-030-58610-2_33
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