Skip to main content
SpringerLink
Log in

LRTI: landmark ratios with task importance toward accurate age estimation using deep neural networks

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Nowadays, age estimation systems have become a pressing need in several vital fields such as security services and health systems. Over the past decade, there have been introduced several efforts to build accurate and robust age estimation systems, where deep networks have proved to be the superior leader of machine learning tools. From this point, we propose a system named landmark ratios with task importance (LRTI), which accurately estimates a person’s age using deep neural networks. The proposed system extracts more precise information using the facial landmarks—rather than using only the extracted features inferred by the convolutional neural network —to estimate the age. The proposed system is based on defining the purposeful characteristics that distinguish the different age classes. As a result, LRTI computes the ratio of distances between the facial landmarks to represent the facial stretching through aging. These distance ratios are added to the network to precisely differentiate the age classes. The proposed system takes into account the in-between relation of age labels which enhance the accuracy of the age estimation process. The in-between relation of age labels is addressed by generating an importance vector, which gives a weight for each class label according to the degree of neighborhood to the target label. From the conducted experiments, the LRTI system adequately models the ordering and continuity properties of the aging process; thus, it has outperformed other state-of-the-art approaches when applied onto MORPH II, FGNET, CACD, AFAD, and UTKFace datasets. LRTI achieved the best mean absolute error, reaching 2.58 with MORPH II, 2.51 with FGNET, 5.39 with CACD, 3.44 with AFAD, and 5.14 with UTKFace.

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

Similar content being viewed by others

References

  1. Angulu R, Tapamo JR, Adewumi AO (2018) Age estimation via face images: a survey. EURASIP J Image Video Process 1:1–35. https://doi.org/10.1186/s13640-018-0278-6

    Article  Google Scholar 

  2. Osman OF, Yap MH (2018) Computational intelligence in automatic face age estimation: a survey. IEEE Trans Emerg Topics Comput Intell 3(3):271–285. https://doi.org/10.1109/TETCI.2016.2646278

    Article  Google Scholar 

  3. Niu Z, Zhou M, Wang L, Gao X, Hua G (2016) Ordinal regression with multiple output cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA,pp. 4920–4928. https://doi.org/10.1109/CVPR.2016.532

  4. Yang HF, Lin BY, Chang KY, Chen CS (2018) Joint estimation of age and expression by combining scattering and convolutional networks. ACM Trans Multimed Comput Commun Appl (TOMM)14(1): 1–18. https://doi.org/10.1145/3152118

  5. Duan M, Li K, Li K (2017) An ensemble CNN2ELM for age estimation. IEEE Trans Inf Forensics Secur 13(3):758–772. https://doi.org/10.1109/TIFS.2017.2766583

    Article  Google Scholar 

  6. Yang HF, Lin BY, Chang KY, Chen CS (2013) Automatic age estimation from face images via deep ranking. Networks 35(8): 1872–1886. https://doi.org/10.5244/C.29.55

    Article  Google Scholar 

  7. Li W., Lu J., Feng J., Xu C., Zhou J., Tian Q. (2019) Bridgenet: a continuity-aware probabilistic network for age estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 1145–1154. https://doi.org/10.1109/CVPR.2019.00124

  8. Wang X, Guo R, Kambhamettu C (2015) Deeply-learned feature for age estimation. In: Proceedings of IEEE winter conference on applications of computer vision, Waikoloa, HI, USA, pp. 534–541. https://doi.org/10.1109/WACV.2015.77

  9. Agbo-Ajala O, Viriri S (2021) Deep learning approach for facial age classification: a survey of the state-of-the-art. Artif Intell Rev 54(1):179–213. https://doi.org/10.1007/s10462-020-09855-0

    Article  Google Scholar 

  10. Cao W., Mirjalili V., Raschka S. (2019) Rank-consistent ordinal regression for neural networks. Machine Learning, 1(6), 13: 325–331. arXiv:1901.07884https://doi.org/10.1016/j.patrec.2020.11.008

  11. Xie JC, Pun CM (2020) Deep and ordinal ensemble learning for human age estimation from facial images. IEEE Trans Inf Forensics Secur 15:2361–2374. https://doi.org/10.1109/TIFS.2020.2965298

    Article  Google Scholar 

  12. Liu KH, Liu TJ (2019) A structure-based human facial age estimation framework under a constrained condition. IEEE Trans Image Process 28(10):5187–5200. https://doi.org/10.1109/TIP.2019.2916768

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen S., Zhang C., Dong M., Le J., Rao M. (2017) Using ranking-cnn for age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp. 5183–5192.https://doi.org/10.1109/CVPR.2017.86

  14. Taheri S, Toygar Ö (2019) On the use of DAG-CNN architecture for age estimation with multi-stage features fusion. Neurocomputing 329: 300–310.https://doi.org/10.1016/j.neucom.2018.10.071

    Article  Google Scholar 

  15. Zhang C, Liu S, Xu X, Zhu C (2019) C3AE: exploring the limits of compact model for age estimation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Long Beach, CA, USA, pp. 12587–12596. https://doi.org/10.1109/CVPR.2019.01287

  16. Agbo-Ajala O, Viriri S (2020) Deeply learned classifiers for age and gender predictions of unfiltered faces. Sci World J. https://doi.org/10.1155/2020/1289408

    Article  Google Scholar 

  17. Li D, Ma X, Ren Y, Teng SW (2020) Rectified softmax loss with all-sided cost sensitivity for age estimation. IEEE Access 8:32551–32563. https://doi.org/10.1109/ACCESS.2020.2964281

    Article  Google Scholar 

  18. Liu N, Zhang F, Duan F (2020) Facial age estimation using a multi-task network combining classification and regression. IEEE Access 8:92441–92451. https://doi.org/10.1109/ACCESS.2020.2994322

    Article  Google Scholar 

  19. Badr MM, Sarhan AM, Elbasiony RM (2019) Facial age estimation using deep neural networks: a survey. In 2019 15th international computer engineering conference (ICENCO), IEEE, Cairo, Eygpt, pp. 183–191. https://doi.org/10.1109/ICENCO48310.2019.9027363

  20. Li J, Lu B-L (2009) An adaptive image Euclidean distance. Pattern Recogn 42:349–357. https://doi.org/10.1016/j.patcog.2008.07.017

    Article  MATH  Google Scholar 

  21. Chen XW, Jeong JC (2007) Enhanced recursive feature elimination. In: Sixth international conference on machine learning and applications (ICMLA 2007), IEEE, Cincinnati, OH, USA, pp. 429–435. https://doi.org/10.1109/ICMLA.2007.35

  22. Jäntschi L (2019) A test detecting the outliers for continuous distributions based on the cumulative distribution function of the data being tested. Symmetry 11(6):835. https://doi.org/10.3390/sym11060835

    Article  Google Scholar 

  23. Ricanek K, Tesafaye T (2006) Morph: a longitudinal image database of normal adult age-progression. In: Proceedings of IEEE 7th international conference on automatic face gesture recognition, Southampton, UK, pp. 341–345. https://doi.org/10.1109/FGR.2006.78

  24. Crowley JL, Cootes T (2009) FGNET, Face and gesture recognition working group. http://www-prima.inrialpes.fr/FGnet/

  25. Chen BC, Chen CS, Hsu WH (2014) Cross-age reference coding for age-invariant face recognition and retrieval. In: European conference on computer vision, Springer, Zurich, Switzerland, pp. 768–783. https://doi.org/10.1007/978-3-319-10599-4_49

  26. Zhang Z, Song Y, Qi H (2017) Age progression/regression by conditional adversarial autoencoder. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, HI, USA, pp. 5810–5818. https://doi.org/10.1109/CVPR.2017.463

  27. Raschka S (2018) MLxtend: providing machine learning and data science utilities and extensions to Python’s scientific computing stack. J Open Source Soft 3(24): 638. https://doi.org/10.21105/joss.00638

  28. Pan H, Han H, Shan S, Chen X (2018) Mean-variance loss for deep age estimation from a face. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp. 5285–5294. https://doi.org/10.1109/CVPR.2018.00554

  29. Zeng X, Huang J, Ding C (2020) Soft-ranking label encoding for robust facial age estimation. IEEE Access 8:134209–134218. https://doi.org/10.1109/ACCESS.2020.3010815

    Article  Google Scholar 

  30. Rothe R, Timofte R, Van Gool L (2018) Deep expectation of real and apparent age from a single image without facial landmarks. Int J Comput Vision 126(2):144–157. https://doi.org/10.1007/s11263-016-0940-3

    Article  MathSciNet  Google Scholar 

  31. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, IEEE, Las Vegas, NV, USA, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90

  32. Guo Y, Zhang L, Hu Y, He X, Gao J (2016) MS-Celeb-1m: a dataset and benchmark for large-scale face recognition. In: Proceedings of the European conference on computer vision, Amsterdam, The Netherlands, pp. 87–102. https://doi.org/10.1007/978-3-319-46487-9_6

  33. Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. (2019). Pytorch: an imperative style, high-performance deep learning library. Advances in neural information processing systems 32, Vancouver, BC, Canada, pp. 8026–8037. https://proceedings.neurips.cc/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf

  34. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: The 3rd International conference on learning representations, San Diego, CA, USA. arxiv: 1412.6980..

  35. Chang KY, Chen CS (2015) A learning framework for age rank estimation based on face images with scattering transform. IEEE Trans Image Process 24(3):785–798. https://doi.org/10.1109/TIP.2014.2387379

    Article  MathSciNet  MATH  Google Scholar 

  36. Yoo B, Kwak Y, Kim Y, Choi C, Kim J (2018) Deep facial age estimation using conditional multitask learning with weak label expansion. IEEE Signal Process Lett 25:808–812. https://doi.org/10.1109/LSP.2018.2822241

    Article  Google Scholar 

  37. Li K, Xing J, Su C, Hu W, Zhang Y, Maybank S (2018) Deep cost-sensitive and order-preserving feature learning for cross-population age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, USA, pp. 399–408. https://doi.org/10.1109/CVPR.2018.00049

  38. Xia M, Zhang X, Liu W, Weng L, Xu Y (2020) Multi-stage feature constraints learning for age estimation. IEEE Trans Inf Forensics Secur 15(1):2417–2428. https://doi.org/10.1109/TIFS.2020.2969552

    Article  Google Scholar 

  39. Liu H, Lu J, Feng J, Zhou J (2018) Label-sensitive deep metric learning for facial age estimation. IEEE Trans Inf Forensics Secur 13(2):292–305. https://doi.org/10.1109/TIFS.2017.2746062

    Article  Google Scholar 

  40. Tan Z, Wan J, Lei Z, Zhi R, Guo G, Li SZ (2018) November) Efficient Groupn encoding and decoding for facial age estimation. IEEE Trans Pattern Anal Mach Intell 40(11):2610–2623. https://doi.org/10.1109/TPAMI.2017.2779808

    Article  Google Scholar 

  41. Gao BB, Zhou HY, Wu J, Geng X (2018) Age estimation using expectation of label distribution learning. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, pp 712–718. https://doi.org/10.24963/ijcai.2018/99.

  42. Li P, Hu Y, He R, Sun . (2018) A coupled evolutionary network for age estimation. Computer Vision and Pattern Recognition, arXiv:1809.07447

  43. Duan M, Li K, Ouyang A, Win K, Li K, Tian Q (2020) EGroupNet: a feature-enhanced network for age estimation with novel age group schemes. ACM Trans Multimed Comput Commun Appl 16(2):1–23. https://doi.org/10.1145/3379449

    Article  Google Scholar 

  44. Chang KY, Chen CS, Hung YP (2011) Ordinal hyper-planes ranker with cost sensitivities for age estimation. In: Computer vision and pattern recognition (CVPR), IEEE, Colorado Springs, CO, USA, pp. 585–592. https://doi.org/10.1109/CVPR.2011.5995437

Download references

Author information

Authors and Affiliations

  1. Department of Computers and Control Engineering, Faculty of Engineering, Tanta University, Tanta, 37133, Egypt

    Marwa M. Badr, Reda M. Elbasiony & Amany M. Sarhan

Authors
  1. Marwa M. Badr
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Reda M. Elbasiony
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amany M. Sarhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marwa M. Badr.

Ethics declarations

Conflict of interest

There are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Badr, M.M., Elbasiony, R.M. & Sarhan, A.M. LRTI: landmark ratios with task importance toward accurate age estimation using deep neural networks. Neural Comput & Applic 34, 9647–9659 (2022). https://doi.org/10.1007/s00521-022-06955-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-022-06955-6

Keywords

Search

Navigation