Facial Aging Prediction Challenges and Developments: A Review

Abstract

Our world today is full of development and people have become obsessed with beauty and maintaining the continuity of youth and fear of facial aging, which represents gradual changes in physiological functions. Hence, the need to simulate facial aging has emerged. Recently, many contributions have spread that have addressed this phenomenon. In this paper, we highlight many facial aging methodologies: the traditional approach, the deep learning approach, and the hybrid model approach. Regarding the feature-based approach, we will explain its strengths and weaknesses. Then we will highlight the methodologies based on deep learning, especially the competitive generative networks that have improved the accuracy and realism of facial aging images. We will also explain the strengths, weaknesses, and possible improvement. In addition, we will mention the hybrid models and their strengths and weaknesses. The datasets used in this work are presented. Furthermore, we will explain how to deal with the challenges associated with facial aging estimation, starting with the obstacles that accompany the dataset, warnings about privacy and ethical considerations, with the need to address these challenges to ensure the safe and ethical use of the techniques, and discuss the impact of the unbalanced distribution of the dataset, and touch on some of the criteria for evaluating these methods. Finally, we present a future vision on the emerging trends, challenges and future directions in the field of facial aging in order to help guide future research in the right direction.

Country : Iraq

1 Amina Taha ALazawe2 Yusra Faisal Mohammad

  1. Computer Science Department, College of Computer Science and Mathematics, University of Mosul, Nineveh, Iraq
  2. Computer Science Department, College of Education for Pure Science, University of Mosul, Nineveh, Iraq

IRJIET, Volume 9, Issue 4, April 2025 pp. 33-49

doi.org/10.47001/IRJIET/2025.904006

References

  1. I.K. Zaal and Y. F. Mohammad, “Machine Learning Algorithms for Human Smoking Behavior Detection using speech,” in 2023 16th International Conference on Developments in eSystems Engineering (DeSE), IEEE, 2023, pp. 298–302.
  2. P. K. Chandaliya and N. Nain, “AW-GAN: face aging and rejuvenation using attention with wavelet GAN,” Neural Comput. Appl., vol. 35, no. 3, pp. 2811–2825, 2023.
  3. W. Yao, M. A. Farooq, J. Lemley, and P. Corcoran, “Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms,” arXiv Prepr. arXiv2406.06932, 2024.
  4. D. Deb, N. Nain, and A. K. Jain, “Longitudinal study of child face recognition,” in 2018 International Conference on Biometrics (ICB), IEEE, 2018, pp. 225–232.
  5. P. K. Chandaliya, A. Sinha, and N. Nain, “Childface: Gender aware child face aging,” in 2020 International Conference of the Biometrics Special Interest Group (BIOSIG), IEEE, 2020, pp. 1–5.
  6. J. Wahid, F. Zhan, P. Rao, and C. Theobalt, “DiffAge3D: Diffusion-based 3D-aware Face Aging,” arXiv Prepr. arXiv2408.15922, 2024.
  7. Q. Teng, R. Wang, X. Cui, P. Li, and Z. He, “Exploring 3D-aware lifespan face aging via disentangled shape-texture representations,” in 2024 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2024, pp. 1–6.
  8. H. Pranoto, Y. Heryadi, H. L. H. S. Warnars, and W. Budiharto, “Recent generative adversarial approach in face aging and dataset review,” IEEE Access, vol. 10, pp. 28693–28716, 2022.
  9. S. Li et al., “ID $^ 3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition,” arXiv Prepr. arXiv2409.17576, 2024.
  10. S. Palsson, E. Agustsson, R. Timofte, and L. Van Gool, “Generative adversarial style transfer networks for face aging,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 2084–2092.
  11. C. N. Duong et al., “Automatic face aging in videos via deep reinforcement learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 10013–10022.
  12. G. Wu et al., “ACGAN: Age‐compensated makeup transfer based on homologous continuity generative adversarial network model,” IET Comput. Vis., vol. 17, no. 5, pp. 537–548, 2023.
  13. I.Kemelmacher-Shlizerman, S. Suwajanakorn, and S. M. Seitz, “Illumination-aware age progression,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 3334–3341.
  14. X. Shu, J. Tang, H. Lai, L. Liu, and S. Yan, “Personalized age progression with aging dictionary,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 3970–3978.
  15. S. Yamamoto, P. A. Savkin, T. Kato, S. Furukawa, and S. Morishima, “Facial video age progression considering expression change,” in Proceedings of the Computer Graphics International Conference, 2017, pp. 1–5.
  16. J. Tang, Z. Li, H. Lai, L. Zhang, and S. Yan, “Personalized age progression with bi-level aging dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 4, pp. 905–917, 2017.
  17. A.Elmahmudi and H. Ugail, “A framework for facial age progression and regression using exemplar face templates,” Vis. Comput., vol. 37, no. 7, pp. 2023–2038, 2021.
  18. A.Sinha and S. Barde, “Face recognition across age progression by using PCA,” Int. J. Food Nutr. Sci., vol. 11, pp. 4608–4617, 2022.
  19. A.Sinha and S. Barde, “Age Invariant Face Recogntion Using Pca And Msvm,” J. Pharm. Negat. Results, pp. 2174–2185, 2022.
  20. Z. Zhang, Y. Song, and H. Qi, “Age progression/regression by conditional adversarial autoencoder,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5810–5818.
  21. M. Ghayoumi, Generative Adversarial Networks in Practice. CRC Press, 2023.
  22. I.Goodfellow et al., “Generative adversarial networks,” Commun. ACM, vol. 63, no. 11, pp. 139–144, 2020.
  23. D. P. Kingma and M. Welling, “Auto-encoding variational Bayes. CoRR, abs/1312.6114,” arXiv Prepr. arXiv1312.6114, 2013.
  24. A.Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adversarial autoencoders,” arXiv Prepr. arXiv1511.05644, 2015.
  25. C. Shi, J. Zhang, Y. Yao, Y. Sun, H. Rao, and X. Shu, “CAN-GAN: Conditioned-attention normalized GAN for face age synthesis,” Pattern Recognit. Lett., vol. 138, pp. 520–526, 2020.
  26. N. Sharma, R. Sharma, and N. Jindal, “An improved technique for face age progression and enhanced super-resolution with generative adversarial networks,” Wirel. Pers. Commun., vol. 114, pp. 2215–2233, 2020.
  27. D. H. Krishna, S. Akshay, A. N. S. Manikumar, and D. H. Sukumar, “Age Vision: AI Powered Facial Age Progression Platform,” 2024.
  28. H. A. Younis and Y. F. Mohammad, “Hybridization of Self Supervised Learning Models for Enhancing Automatic Arabic Speech Recognition,” in 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA), IEEE, 2024, pp. 650–655.
  29. H. A. Younis and Y. F. Mohammad, “Arabic Speech Recognition based on Self Supervised Learning,” in 2023 16th International Conference on Developments in eSystems Engineering (DeSE), IEEE, 2023, pp. 528–533.
  30. Y. Hu, “Face Age Prediction Based on Machine Learning,” Highlights Sci. Eng. Technol., vol. 94, pp. 124–128, 2024.
  31. C. Shi, S. Zhao, K. Zhang, Y. Wang, and L. Liang, “Face-based age estimation using improved Swin Transformer with attention-based convolution,” Front. Neurosci., vol. 17, p. 1136934, 2023.
  32. W. An and G. Wu, “Hybrid spatial-channel attention mechanism for cross-age face recognition,” Electronics, vol. 13, no. 7, p. 1257, 2024.
  33. M. A. Farooq, W. Yao, G. Costache, and P. Corcoran, “Childgan: large scale synthetic child facial data using domain adaptation in stylegan,” IEEE Access, 2023.
  34. K. Ricanek Jr and T. Tesafaye, “MORPH: A longitudinal image Age-progression, of normal adult,” in Proc. 7th Int. Conf. Autom. Face Gesture Recognit, 2006, pp. 0–4.
  35. K. Ricanek and T. Tesafaye, “Morph: A longitudinal image database of normal adult age-progression,” in 7th international conference on automatic face and gesture recognition (FGR06), IEEE, 2006, pp. 341–345.
  36. G. Levi and T. Hassner, “Age and gender classification using convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2015, pp. 34–42.
  37. R. Rothe, R. Timofte, and L. Van Gool, “Deep expectation of real and apparent age from a single image without facial landmarks,” Int. J. Comput. Vis., vol. 126, no. 2, pp. 144–157, 2018.
  38. S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou, “Agedb: the first manually collected, in-the-wild age database,” in proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2017, pp. 51–59.
  39. A.Zargaran, S. Sousi, S. P. Glynou, H. Mortada, D. Zargaran, and A. Mosahebi, “A systematic review of generative adversarial networks (GANs) in plastic surgery,” J. Plast. Reconstr. Aesthetic Surg., vol. 95, pp. 377–385, 2024.
  40. C. Li, Y. Li, Z. Weng, X. Lei, and G. Yang, “Face aging with feature-guide conditional generative adversarial network,” Electronics, vol. 12, no. 9, p. 2095, 2023.
  41. N. A. Sultan and R. P. Qasha, “CONTAINER-BASED VIRTUALIZATION FOR BLOCKCHAIN TECHNOLOGY: A SURVEY.,” Jordanian J. Comput. Inf. Technol., vol. 9, no. 3, 2023.
  42. H. E. Solayman and R. P. Qasha, “On the use of container-based virtualisation for IoT provisioning and orchestration: a survey,” Int. J. Comput. Sci. Math., vol. 18, no. 4, pp. 299–311, 2023.
  43. Y. Galphat, C. Bajaj, G. Amarnani, K. Mulchandani, and J. Repale, “Exploring Techniques For Age Progression In Facial Images: A Comprehensive Survey,” J. Syst. Eng. Electron. (ISSN NO 1671-1793), vol. 34, no. 5, 2024.
  44. S. Shen, X. Yuan, J. Wang, L. Fan, J. Zhao, and J. Tao, “Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods,” Front. Public Heal., vol. 10, p. 1068253, 2022.
  45. M. Suin, N. G. Nair, C. P. Lau, V. M. Patel, and R. Chellappa, “Diffuse and restore: A region-adaptive diffusion model for identity-preserving blind face restoration,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024, pp. 6343–6352.