Skip to main content
SpringerLink
Log in

Age transformation based on deep learning: a survey

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Age transformation aims to preserve personalized facial information while altering a given face to appear at a target age. This technique finds extensive applications in fields such as face recognition, movie special effects, and social entertainment, among others. With the advancement of deep learning, particularly Generative Adversarial Networks (GANs), research on age transformation has made significant progress, leading to the emergence of a diverse range of deep learning-based methods. However, a comprehensive and systematic literature review of these methods is currently lacking. In this survey, we provide an all-encompassing review of deep learning methods for facial aging. Firstly, we summarize the key aspects of feature preservation during the age transformation process. Subsequently, we present a comprehensive overview of facial age transformation techniques, categorized according to various deep learning network architectures. Additionally, we conduct an analysis and comparison of commonly used face image datasets, offering recommendations for dataset selection. Furthermore, we consolidate the qualitative and quantitative evaluation metrics commonly employed in age transformation methodologies through experimental assessment. Finally, we address potential areas of future research in age transformation methods, based on the current challenges and limitations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. Face aging and rejuvenation are applied to predict a person appearance at different ages. Face age progression (prediction of future appearance) and Face age regression (estimation of previous appearance) are also known as face aging and rejuvenation.

References

  1. Alaluf Y, Patashnik O, Cohen-Or D (2021) Only a matter of style: age transformation using a style-based regression model. ACM Trans Graph (TOG) 40(4):1–12

    Article  Google Scholar 

  2. Albert AM, Ricanek K Jr, Patterson E (2007) A review of the literature on the aging adult skull and face: implications for forensic science research and applications. Forensic Sci Int 172(1):1–9

    Article  PubMed  Google Scholar 

  3. Alley TR (2013) Social and applied aspects of perceiving faces. Psychology Press, London

    Book  Google Scholar 

  4. Antipov G, Baccouche M, Dugelay JL (2017) Face aging with conditional generative adversarial networks. In: 2017 IEEE international conference on image processing (ICIP), IEEE, pp 2089–2093

  5. Bando Y, Kuratate T, Nishita T (2002) A simple method for modeling wrinkles on human skin. In: Proceedings of the 10th pacific conference on computer graphics and applications, 2002. IEEE, pp 166–175

  6. Banerjee S, Mittal G, Joshi A, et al (2023) Identity-preserving aging of face images via latent diffusion models. arXiv:2307.08585

  7. Baykal G, Ozcelik F, Unal G (2022) Exploring deshufflegans in self-supervised generative adversarial networks. Pattern Recognit 122(108):244

    Google Scholar 

  8. Berg AC, Justo SC (2003) Aging of orbicularis muscle in virtual human faces. In: Proceedings on 7th international conference on information visualization, 2003. IV 2003. IEEE, pp 164–168

  9. Bińkowski M, Sutherland D, Arbel M, et al (2018) Demystifying mmd gans. ICLR

  10. Boussaad L, Boucetta A (2022) Deep-learning based descriptors in application to aging problem in face recognition. J King Saud Univ Comput Inf Sci 34(6):2975–2981

    Google Scholar 

  11. Chandaliya PK, Nain N (2022) Childgan: face aging and rejuvenation to find missing children. Pattern Recognit 129(108):761

    Google Scholar 

  12. Chandaliya PK, Nain N (2023) AW-GAN: face aging and rejuvenation using attention with wavelet GAN. Neural Comput Appl 35(3):2811–2825

    Article  Google Scholar 

  13. Chandaliya PK, Sinha A, Nain N (2020) Childface: Gender aware child face aging. In: 2020 International conference of the biometrics special interest group (BIOSIG). IEEE, pp 1–5

  14. Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: Computer vision–ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part VI 13, Springer, pp 768–783

  15. Chen K, Yao L, Zhang D et al (2019) A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans Neural Netw Learn Syst 31(5):1747–1756

    Article  PubMed  Google Scholar 

  16. Chen X, Lathuilière S (2023) Face aging via diffusion-based editing. arXiv:2309.11321

  17. Dahlan HA (2021) A survey on deep learning face age estimation model: method and ethnicity. Int J Adv Comput Sci Appl. https://doi.org/10.14569/ijacsa.2021.0121111

    Article  Google Scholar 

  18. Deb D, Aggarwal D, Jain AK (2021) Identifying missing children: face age-progression via deep feature aging. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 10,540–10,547

  19. Deshmukh T, Kokate D (2022) Human face aging based on deep learning: a survey. Available at SSRN 4043509

  20. Despois J, Flament F, Perrot M (2020) AgingMapGAN (AMGAN): high-resolution controllable face aging with spatially-aware conditional GANs. In: Computer vision–ECCV 2020 workshops: glasgow, UK, August 23–28, 2020, Proceedings, Part III 16. Springer, pp 613–628

  21. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Trans Inf Forensics Secur 9(12):2170–2179

    Article  Google Scholar 

  22. Fang H, Deng W, Zhong Y, et al (2020) Triple-GAN: progressive face aging with triple translation loss. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 804–805

  23. Fu Y (2014) Fg-net aging database. https://yanweifu.github.io/FG_NET_data

  24. Fu Y, Xu Y, Huang TS (2007) Estimating human age by manifold analysis of face pictures and regression on aging features. In: 2007 IEEE international conference on multimedia and expo. IEEE, pp 1383–1386

  25. Fu Y, Guo G, Huang TS (2010) Age synthesis and estimation via faces: a survey. IEEE Trans Pattern Anal Mach Intell 32(11):1955–1976

    Article  PubMed  Google Scholar 

  26. Gandhi MR (2004) A method for automatic synthesis of aged human facial images. PhD thesis, McGill University, Montreal, Canada

  27. Gomez-Trenado G, Lathuilière S, Mesejo P et al (2022) Custom structure preservation in face aging. In: Computer vision—ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVI. Springer, pp 565–580

  28. Goodfellow I, Pouget-Abadie J, Mirza M, et al (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, et al (eds), Advances in neural information processing systems, vol 27. Curran Associates, Inc., https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

  29. Grimmer M, Ramachandra R, Busch C (2021) Deep face age progression: a survey. IEEE Access 9:83,376-83,393

    Article  Google Scholar 

  30. Gulrajani I, Ahmed F, Arjovsky M, et al (2017) Improved training of wasserstein gans. In: Advances in neural information processing systems, vol 30

  31. Guo G, Fu Y, Huang TS, et al (2008) Locally adjusted robust regression for human age estimation. In: 2008 IEEE workshop on applications of computer vision, IEEE, pp 1–6

  32. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  33. He S, Liao W, Yang MY, et al (2021) Disentangled lifespan face synthesis. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3877–3886

  34. Heusel M, Ramsauer H, Unterthiner T, et al (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in neural information processing systems, vol 30

  35. Ho J, Jain A, Abbeel P (2020) Denoising diffusion probabilistic models. Adv Neural Inf Process Syst 33:6840–6851

    Google Scholar 

  36. Hore A, Ziou D (2010) Image quality metrics: PSNR vs. SSIM. In: 2010 20th international conference on pattern recognition, IEEE, pp 2366–2369

  37. Horng WB, Lee CP, Chen CW et al (2001) Classification of age groups based on facial features. J Appl Sci Eng 4(3):183–192

    Google Scholar 

  38. Hsu GS, Xie RC, Chen ZT, et al (2022) Agetransgan for facial age transformation with rectified performance metrics. In: European conference on computer vision. Springer, pp 580–595

  39. Huang X, Belongie S (2017) Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE international conference on computer vision, pp 1501–1510

  40. Huang X, Gong M (2022) Landmark-guided conditional gans for face aging. In: Image analysis and processing–ICIAP 2022: 21st international conference, Lecce, Italy, May 23–27, 2022, Proceedings, Part I. Springer, pp 270–283

  41. Huang Z, Chen S, Zhang J et al (2020) PFA-GAN: progressive face aging with generative adversarial network. IEEE Trans Inf Forensics Secur 16:2031–2045

    Article  Google Scholar 

  42. Huang Z, Zhang J, Shan H (2021) When age-invariant face recognition meets face age synthesis: a multi-task learning framework. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 7282–7291

  43. Jeon S, Lee P, Hong K et al (2021) Continuous face aging generative adversarial networks. In: ICASSP 2021-2021 IEEE international conference on acoustics. Speech and signal processing (ICASSP). IEEE, pp 1995–1999

  44. Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4401–4410

  45. Karras T, Laine S, Aittala M, et al (2020) Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8110–8119

  46. Kemelmacher-Shlizerman I, Suwajanakorn S, Seitz SM (2014) Illumination-aware age progression. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3334–3341

  47. Kemmer B, Simões R, Lima C (2022) Face aging using generative adversarial networks. In: Generative adversarial learning: architectures and applications. Springer, pp 145–168

  48. Khajavi M, Ahmadyfard A (2023) Human face aging based on active appearance model using proper feature set. Signal Image Video Process 17(4):1465–1473

    Article  Google Scholar 

  49. Kim YH, Nam SH, Hong SB et al (2022) GRA-GAN: generative adversarial network for image style transfer of gender, race, and age. Expert Syst Appl 198(116):792

    Google Scholar 

  50. Kingma D, Welling M (2013) Auto-encoding variational bayes. ICLR

  51. Klare B, Jain AK (2010) On a taxonomy of facial features. In: 2010 4th IEEE international conference on biometrics: theory, applications and systems (BTAS). IEEE, pp 1–8

  52. Korgialas C, Pantraki E, Bolari A et al (2023) Face aging by explainable conditional adversarial autoencoders. J Imaging 9(5):96

    Article  PubMed  PubMed Central  Google Scholar 

  53. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Article  Google Scholar 

  54. Kuo TH, Jia Z, Kuo TW, et al (2023) Bitrackgan: Cascaded cyclegans to constraint face aging. arXiv:2304.11313

  55. Kwak Jg, Han DK, Ko H (2020) CAFE-GAN: arbitrary face attribute editing with complementary attention feature. In: Computer Vision—ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16. Springer, pp 524–540

  56. Lanitis A, Taylor CJ, Cootes TF (2002) Toward automatic simulation of aging effects on face images. IEEE Trans Pattern Anal Mach Intell 24(4):442–455

    Article  Google Scholar 

  57. Ledig C, Theis L, Huszár F, et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690

  58. Lee J, Yun J, Park S, et al (2021) Improving face recognition with large age gaps by learning to distinguish children. arXiv:2110.11630

  59. Li Z, Jiang R, Aarabi P (2021) Continuous face aging via self-estimated residual age embedding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 15,008–15,017

  60. Liu L, Yu H, Wang S et al (2021) Learning shape and texture progression for young child face aging. Signal Process Image Commun 93(116):127

    ADS  Google Scholar 

  61. Liu S, Sun Y, Zhu D, et al (2017) Face aging with contextual generative adversarial nets. In: Proceedings of the 25th ACM international conference on Multimedia, pp 82–90

  62. Ma W, Zhou Y, He J (2021) Semi-supervised face aging and rejuvenating. J Electron Imaging 30(2):023,003-023,003

    Article  Google Scholar 

  63. Maeng J, Oh K, Suk HI (2023) Age-aware guidance via masking-based attention in face aging. In: 15th ACM international conference on Information and knowledge management

  64. Makhmudkhujaev F, Hong S, Park IK (2021) Re-aging gan: toward personalized face age transformation. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 3908–3917

  65. Mao X, Li Q, Xie H, et al (2017) Least squares generative adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2794–2802

  66. Martin V, Seguier R, Porcheron A et al (2019) Face aging simulation with a new wrinkle oriented active appearance model. Multimedia Tools Appl 78:6309–6327

    Article  Google Scholar 

  67. Megvii Incorporated (2012) Face++ research toolkit. http://www.faceplusplus.com

  68. Mendelson B, Wong CH (2013) Anatomy of the aging face. Plastic Surg 2:78–92

    Google Scholar 

  69. Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv:1411.1784

  70. Mishra A, Krishna Reddy S, Mittal A, et al (2018) A generative model for zero shot learning using conditional variational autoencoders. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 2188–2196

  71. Moschoglou S, Papaioannou A, Sagonas C, et al (2017) Agedb: the first manually collected, in-the-wild age database. In: proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 51–59

  72. Nagisetty V, Graves L, Scott J, et al (2020) xai-gan: Enhancing generative adversarial networks via explainable ai systems. arXiv:2002.10438

  73. Nickabadi A, Fard MS, Farid NM, et al (2022) A comprehensive survey on semantic facial attribute editing using generative adversarial networks. arXiv:2205.10587

  74. Or-El R, Sengupta S, Fried O, et al (2020) Lifespan age transformation synthesis. In: Computer Vision–ECCV 2020: 16th European conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16. Springer, pp 739–755

  75. Othmani A, Taleb AR, Abdelkawy H et al (2020) Age estimation from faces using deep learning: a comparative analysis. Comput Vis Image Understand 196(102):961

    Google Scholar 

  76. Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32(5):947–954

    Article  PubMed  Google Scholar 

  77. Pranoto H, Heryadi Y, Warnars HLHS et al (2022) Recent generative adversarial approach in face aging and dataset review. IEEE Access 10:28,693-28,716

    Article  Google Scholar 

  78. Ramanathan N, Chellappa R (2006) Modeling age progression in young faces. In: 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR’06). IEEE, pp 387–394

  79. Rexbye H, Petersen I, Johansens M et al (2006) Influence of environmental factors on facial ageing. Age Ageing 35(2):110–115

    Article  PubMed  Google Scholar 

  80. Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: 7th international conference on automatic face and gesture recognition (FGR06). IEEE, pp 341–345

  81. Riccio D, Tortora G, De Marsico M, et al (2012) Ega-ethnicity, gender and age, a pre-annotated face database. In: 2012 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS) proceedings. IEEE, pp 1–8

  82. Rombach R, Blattmann A, Lorenz D, et al (2022) High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 10,684–10,695

  83. Roomi SMM, Virasundarii S, Selvamegala S, et al (2011) Race classification based on facial features. In: 2011 3rd national conference on computer vision, pattern recognition, image processing and graphics. IEEE, pp 54–57

  84. Rostami M, Farajollahi A, Parvin H (2022) Deep learning-based face detection and recognition on drones. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-022-03897-8

    Article  Google Scholar 

  85. Salimans T, Goodfellow I, Zaremba W, et al (2016) Improved techniques for training gans. In: Advances in neural information processing systems, vol 29

  86. Shu X, Xie GS, Li Z et al (2016) Age progression: current technologies and applications. Neurocomputing 208:249–261

    Article  Google Scholar 

  87. Simonite T (2006) Virtual face-ageing may help find missing persons. NewScientist com. http://technologynewscientist-com/channel/tech/forensic-science

  88. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  89. Sohl-Dickstein J, Weiss E, Maheswaranathan N, et al (2015) Deep unsupervised learning using nonequilibrium thermodynamics. In: International conference on machine learning, PMLR, pp 2256–2265

  90. Song J, Zhang J, Gao L, et al (2018) Dual conditional gans for face aging and rejuvenation. In: IJCAI, pp 899–905

  91. Song J, Zhang J, Gao L et al (2021) AgeGAN++: Face aging and rejuvenation with dual conditional GANs. IEEE Trans Multimedia 24:791–804

    Article  Google Scholar 

  92. Song Y, Sohl-Dickstein J, Kingma DP, et al (2020) Score-based generative modeling through stochastic differential equations. arXiv:2011.13456

  93. Sun Y, Wang X, Tang X (2013) Deep convolutional network cascade for facial point detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3476–3483

  94. Suo J, Zhu SC, Shan S et al (2009) A compositional and dynamic model for face aging. IEEE Trans Pattern Anal Mach Intell 32(3):385–401

    Google Scholar 

  95. Sveikata K, Balciuniene I, Tutkuviene J et al (2011) Factors influencing face aging: literature review. Stomatologija 13(4):113–116

    PubMed  Google Scholar 

  96. Wang W, Cui Z, Yan Y, et al (2016) Recurrent face aging. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2378–2386

  97. Wang Z, Tang X, Luo W, et al (2018) Face aging with identity-preserved conditional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7939–7947

  98. Xiao T, Xu Y, Yang K, et al (2015) The application of two-level attention models in deep convolutional neural network for fine-grained image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 842–850

  99. Xu K, Ba J, Kiros R, et al (2015) Show, attend and tell: neural image caption generation with visual attention. In: International conference on machine learning, PMLR, pp 2048–2057

  100. Yang C, Lv Z (2020) Gender based face aging with cycle-consistent adversarial networks. Image Vis Comput 100(103):945

    Google Scholar 

  101. Yang H, Huang D, Wang Y, et al (2018a) Learning face age progression: a pyramid architecture of gans. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 31–39

  102. Yang TY, Huang YH, Lin YY, et al (2018b) Ssr-net: A compact soft stagewise regression network for age estimation. In: IJCAI, p 7

  103. Yao X, Puy G, Newson A, et al (2021) High resolution face age editing. In: 2020 25th international conference on pattern recognition (ICPR). IEEE, pp 8624–8631

  104. Yin J, Boyce MC (2015) Unique wrinkles as identity tags. Nature 520(7546):164–165

    Article  ADS  CAS  PubMed  Google Scholar 

  105. Zhang H, Goodfellow I, Metaxas D, et al (2019) Self-attention generative adversarial networks. In: International conference on machine learning, PMLR, pp 7354–7363

  106. Zhang R, Isola P, Efros AA, et al (2018) The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 586–595

  107. Zhang Y, Liu L, Li C, et al (2017a) Quantifying facial age by posterior of age comparisons. arXiv:1708.09687

  108. Zhang Z, Song Y, Qi H (2017b) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5810–5818

  109. Zhao J, Cheng Y, Cheng Y, et al (2019) Look across elapse: Disentangled representation learning and photorealistic cross-age face synthesis for age-invariant face recognition. In: Proceedings of the AAAI conference on artificial intelligence, pp 9251–9258

  110. Zhu H, Huang Z, Shan H et al (2020) Look globally, age locally: Face aging with an attention mechanism. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1963–1967

  111. Zhu JY, Park T, Isola P, et al (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  112. Zoran D, Chrzanowski M, Huang PS, et al (2020) Towards robust image classification using sequential attention models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 9483–9492

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (61806071, 62102129) and Natural Science Foundation of Hebei Province (F2019202381, F2019202464).

Author information

Authors and Affiliations

  1. School of Artificial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Yingchun Guo, Xin Su, Gang Yan, Ye Zhu & Xueqi Lv

Authors
  1. Yingchun Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xin Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ye Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xueqi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingchun Guo.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, Y., Su, X., Yan, G. et al. Age transformation based on deep learning: a survey. Neural Comput & Applic 36, 4537–4561 (2024). https://doi.org/10.1007/s00521-023-09376-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09376-1

Keywords

Search

Navigation