ARCHIVES
Original Article
Age Transformation Using Deep Learning Techniques
Saba Naazneen1
Dr. Mohd Rafi Ahmed2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.
Published Online: July-August 2025
Pages: 24-28
Cite this article
↗ https://www.doi.org/10.59256/ijsreat.20250504005
References
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16. J. Deng, J. Guo, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2019, pp. 4690–4699.
17. D. Tran et al., “Learning spatiotemporal features with 3D convolutional networks,” Proc. IEEE Int. Conf. Comput. Vis., 2015, pp. 4489–4497.
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20. J. Yoon, S. Kim, and S. Choi, “Temporal GANs conditioning on action sequences,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 6, pp. 1453–1465, Jun. 2020.
21. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
22. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
23. Y. Luo, J. Ren, and M. Liu, “Face aging and rejuvenation by conditional multi-adversarial autoencoder with ranking loss,” IEEE Access, vol. 8, pp. 127638–127651, 2020.
24. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
25. Streamlit, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
2. Z. Zhang, Y. Song, and H. Qi, “Age progression/regression by conditional adversarial autoencoder,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 5810–5818.
3. T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), 2019, pp. 4401–4410.
4. I. Goodfellow et al., “Generative adversarial nets,” Adv. Neural Inf. Process. Syst. (NeurIPS), vol. 27, 2014, pp. 2672–2680.
5. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2016, pp. 770–778.
6. Y. Li, S. Liu, J. Yang, and M.-H. Yang, “Generative face completion,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 2017, pp. 3911–3919.
7. M. Liu et al., “Dual discriminator GANs for semantic image synthesis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 11, pp. 3693–3706, Nov. 2021.
8. A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional GANs,” arXiv preprint arXiv:1511.06434, 2015.
9. H. Huang et al., “Age-invariant face recognition with deep adversarial learning,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 9, pp. 2363–2373, 2018.
10. Y. Yang, D. Huang, Y. Wang, and Y. Wang, “Learning structured age distribution for facial age estimation,” IEEE Trans. Image Process., vol. 27, no. 9, pp. 4526–4538, 2018.
11. A. Lanitis, C. J. Taylor, and T. F. Cootes, “Toward automatic simulation of aging effects on face images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 4, pp. 442–455, Apr. 2002.
12. S. E. Choi, Y. J. Kim, N. S. Moon, and K. R. Park, “Age estimation using a hierarchical classifier based on global and local facial features,” Pattern Recognit., vol. 44, no. 6, pp. 1262–1281, 2011.
13. X. Wu, R. He, Z. Sun, and T. Tan, “A light CNN for deep face representation with noisy labels,” IEEE Trans. Inf. Forensics Secur., vol. 13, no. 11, pp. 2884–2896, Nov. 2018.
14. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Process. Lett., vol. 23, no. 10, pp. 1499–1503, Oct. 2016.
15. A. Jourabloo, M. Ye, X. Liu, and L. Ren, “Pose-invariant face alignment with a single CNN,” Proc. IEEE Int. Conf. Comput. Vis., 2017, pp. 3200–3209.
16. J. Deng, J. Guo, and S. Zafeiriou, “ArcFace: Additive angular margin loss for deep face recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2019, pp. 4690–4699.
17. D. Tran et al., “Learning spatiotemporal features with 3D convolutional networks,” Proc. IEEE Int. Conf. Comput. Vis., 2015, pp. 4489–4497.
18. J. Zhao, M. Mathieu, and Y. LeCun, “Energy-based generative adversarial network,” arXiv preprint arXiv:1609.03126, 2016.
19. H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, and W. Liu, “CosFace: Large margin cosine loss for deep face recognition,” Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2018, pp. 5265–5274.
20. J. Yoon, S. Kim, and S. Choi, “Temporal GANs conditioning on action sequences,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 6, pp. 1453–1465, Jun. 2020.
21. M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv preprint arXiv:1701.07875, 2017.
22. S. Karras et al., “Training generative adversarial networks with limited data,” Proc. NeurIPS, vol. 33, pp. 12104–12114, 2020.
23. Y. Luo, J. Ren, and M. Liu, “Face aging and rejuvenation by conditional multi-adversarial autoencoder with ranking loss,” IEEE Access, vol. 8, pp. 127638–127651, 2020.
24. OpenCV, “Open Source Computer Vision Library,” [Online]. Available: https://opencv.org
25. Streamlit, “Streamlit Documentation,” [Online]. Available: https://docs.streamlit.io/
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