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PFA-GAN: Pose Face Augmentation Based on Generative Adversarial Network
Volume 32, Issue 2 (2021), pp. 425–440
Pub. online: 29 January 2021      Type: Research Article      Open accessOpen Access
Received
1 June 2020
Accepted
1 January 2021
Published
29 January 2021

Abstract

In this work, we propose a novel framework based on Generative Adversarial Networks for pose face augmentation (PFA-GAN). It enables a controlled pose synthesis of a new face image from a source face given a driving one while preserving the identity of the source face. We introduce a method for training the framework in a fully self-supervised mode using a large-scale dataset of unconstrained face images. Besides, some augmentation strategies are presented to expand the training set. The face verification experimental results demonstrate the effectiveness of the presented augmentation strategies as all augmented datasets outperform the baseline.

References

 
Antoniou, A., Storkey, A., Edwards, H. (2018). Data Augmentation Generative Adversarial Networks. Iclr.
 
Blanz, V., Vetter, T. (1999). A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH, 1999.
 
Cao, J., Hu, Y., Yu, B., He, R., Sun, Z. (2018a). Load balanced GANs for multi-view face image synthesis. abs/1802.07447.
 
Cao, Q., Shen, L., Xie, W., Parkhi, O.M., Zisserman, A. (2018b). VGGFace2: a dataset for recognising faces across pose and age. In: Proceedings – 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018.
 
Crispell, D., Biris, O., Crosswhite, N., Byrne, J., Mundy, J.L. (2017). Dataset augmentation for pose and lighting invariant face recognition. arXiv:1704.04326 [cs.CV].
 
Deng, J., Guo, J., Xue, N., Zafeiriou, S. (2019). ArcFace: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
 
Farahani, A., Mohseni, H. (2019). Multi-pose face recognition using pairwise supervised dictionary learning. Informatica, 30, 647–670.
 
Feng, Z.H., Kittler, J., Christmas, W., Huber, P., Wu, X.J. (2017). Dynamic attention-controlled cascaded shape regression exploiting training data augmentation and fuzzy-set sample weighting. In: Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
 
Guo, Y., Zhang, L., Hu, Y., He, X., Gao, J. (2016). MS-celeb-1M: a dataset and benchmark for large-scale face recognition. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
 
Guo, Y., Zhang, J., Cai, J., Jiang, B., Zheng, J. (2017). 3DFaceNet: real-time dense face reconstruction via synthesizing photo-realistic face images. arXiv:1708.00980.
 
He, K., Zhang, X., Ren, S., Sun, J. (2016). Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
 
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E. (2007). Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Tech. Rep. 07-49, University of Massachusetts, Amherst.
 
Huang, R., Zhang, S., Li, T., He, R. (2017). 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.
 
Johnson, J., Alahi, A., Fei-Fei, L. (2016). Perceptual losses for real-time style transfer and super-resolution. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
 
Kingma, D.P., Ba, J.L. (2015). Adam: a method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings.
 
Moschoglou, S., Papaioannou, A., Sagonas, C., Deng, J., Kotsia, I., Zafeiriou, S. (2017). AgeDB: the first manually collected, in-the-wild age database. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.
 
Parkhi, O.M., Vedaldi, A., Zisserman, A. (2015). Deep face recognition. In: British Machine Vision Conference, Vol. 1, pp. 41.1–41.12.
 
Ribarić, S., Fratrić, I., Kiš, K. (2008). A novel biometric personal verification system based on the combination of palmprints and faces. Informatica, 19(1), 81–100.
 
Sengupta, S., Chen, J.C., Castillo, C., Patel, V.M., Chellappa, R., Jacobs, D.W. (2016). Frontal to profile face verification in the wild. In: 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016.
 
Simonyan, K., Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations, ICLR 2015 – Conference Track Proceedings.
 
Tian, Y., Peng, X., Zhao, L., Zhang, S., Metaxas, D.N. (2018). CR-GAN: learning complete representations for multi-view generation. In: IJCAI International Joint Conference on Artificial Intelligence.
 
Tran, L., Yin, X., Liu, X. (2017). Disentangled representation learning GAN for pose-invariant face recognition. In: Proceedings – 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017.
 
Yin, X., Yu, X., Sohn, K., Liu, X., Chandraker, M. (2017). Towards large-pose face Frontalization in the wild. In: Proceedings of the IEEE International Conference on Computer Vision.
 
Zeno, B., Kalinovskiy, I., Matveev, Y. (2019a). IP-GAN: learning identity and pose disentanglement in generative adversarial networks. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
 
Zeno, B.H., Kalinovskiy, I.A., Matveev, Y.N. (2019b). Identity preserving face synthesis using generative adversarial networks. In: ACM International Conference Proceeding Series.
 
Zhang, K., Zhang, Z., Li, Z., Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499–1503.
 
Zheng, T., Deng, W. (2018). Cross-pose LFW: a database for studying cross-pose face recognition in unconstrained environments.
 
Zheng, T., Deng, W., Hu, J. (2017). Cross-age LFW: a database for studying cross-age face recognition in unconstrained environments. ArXiv: abs/1708.08197.
 
Zhu, X., Lei, Z., Liu, X., Shi, H., Li, S.Z. (2016). Face alignment across large poses: a 3D solution. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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Copyright
© 2021 Vilnius University
Open access article under the CC BY license.

Keywords
generative adversarial networks face verification visual data augmentation

Funding
This work was financially supported by the Government of the Russian Federation (Grant 08-08).

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