The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Face aging with conditional generative adversarial networks
Open accessAbstract
It has been recently shown that Generative Adversarial Networks (GANs) can produce synthetic images of exceptional visual fidelity. In this work, we propose the first GAN-based method for automatic face aging. Contrary to previous works employing GANs for altering of facial attributes, we make a particular emphasize on preserving the original person's identity in the aged version of his/her face. To this end, we introduce a novel approach for “Identity-Preserving” optimization of GAN's latent vectors. The objective evaluation of the resulting aged and rejuvenated face images by the state-of-the-art face recognition and age estimation solutions demonstrate the high potential of the proposed method.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
FaceNet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., & Philbin, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298682 · Cited by 15,267
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Research works printed but not linked: 17
- lindbo larsen, 2016, Autoencoding beyond pixels using a learned similarity metric, Proceedings of International Conference on Machine Learning
- perarnau, 2016, Invertible conditional gans for image editing, Proceedings of advances in Neural Information Processing Systems Workshops
- gauthier, 2014, Conditional generative adversarial nets for convolutional face generation, Class Project for Stanford CS231N Convolutional Neural Networks for Visual Recognition
- 10.1007/978-3-319-46454-1_36 · DOI · Paper page
- 10.1137/0916069 · DOI · Paper page
- 10.1109/CVPR.2016.261 · DOI
- 10.1109/TPAMI.2010.14 · DOI · Paper page
- 10.1109/CVPR.2014.426 · DOI · Paper page
- 10.1109/38.946630 · DOI · Paper page
- 10.1145/2342896.2343002 · DOI
- suo, 2010, A compositional and dynamic model for face aging, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 385, 10.1109/TPAMI.2009.39 · DOI · Paper page
- 10.1109/ICCV.2015.452 · DOI
- fu, 2010, Age synthesis and estimation via faces: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 1955, 10.1109/TPAMI.2010.36 · DOI · Paper page
- goodfellow, 2015, Generative adversarial nets, Proceedings of Advances in Neural Information Processing Systems
- 10.1109/CVPRW.2016.105 · DOI
- amos, 2016, Openface: A general-purpose face recognition library with mobile applications, Tech Rep CM-CS-16-118 CMU School of Computer Science
- 10.1109/ICCVW.2015.41 · DOI · Paper page
Cited by
FaceForensics++: Learning to Detect Manipulated Facial Images
Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., & Niessner, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00009 · Cited by 3,314
Deepfake Video Detection Using Recurrent Neural Networks
Guera, D., & Delp, E. J. (2018) · 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2018.8639163 · Cited by 1,285
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet · doi:10.3390/fi15080260 · Cited by 638
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598
Metadata
- Authors
- Antipov, G., Baccouche, M., & Dugelay, J.-L.
- Venue
- 2017 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 2089-2093
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2587706859
- Cited by
- 515
- References
- 5
- FWCI
- 16.66
- Type
- conference paper
- Language
- en
- Open access
- green
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- Antipov, G., Baccouche, M., & Dugelay, J.-L. (2017). Face aging with conditional generative adversarial networks. 2017 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip.2017.8296650
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