Skip to main content
SpringerLink
Log in

A comparative study of human facial age estimation: handcrafted features vs. deep features

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In recent times, the topic of human facial age estimation attracted much attention. This is due to its ability to improve biometrics systems. Recently, several applications that are based on the demographic attributes estimation have been developed. These include law enforcement, re-identification in videos, planed marketing, intelligent advertising, social media, and human-computer interaction. The main contributions of the paper are as follows. Firstly, it extends some handcrafted models that are based on the Pyramid Multi Level (PML) face representation. Secondly, it evaluates the performance of two different kinds of features that are handcrafted and deep features. It compares handcrafted and deep features in terms of accuracy and computational complexity. The proposed scheme of study includes the following three main steps: 1) face preprocessing; 2) feature extraction (two different kinds of features are studied: handcrafted and deep features); 3) feeding the obtained features to a linear regressor. In addition, we investigate the strengths and weaknesses of handcrafted and deep features when used in facial age estimation. Experiments are run on three public databases (FG-NET, PAL and FACES). These experiments show that both handcrafted and deep features are effective for facial age estimation.

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

Similar content being viewed by others

References

  1. Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: Application to face recognition. IEEE Trans Pattern Anal Mach Intell (12) 2037–2041

  2. Alley ST (1988) Applied aspects of perceiving faces resources for ecological psychology Lawrence Erlbaum associates

  3. Angulu R, Tapamo JR, Adewumi AO (2018) Age estimation via face images: a survey. EURASIP J Image Video Process 2018(1):42

    Article  Google Scholar 

  4. Bekhouche S, Ouafi A, Taleb-Ahmed A, Hadid A, Benlamoudi A (2014) Facial age estimation using bsif and lbp. In: Proceeding of the first International Conference on Electrical Engineering ICEEB’14

  5. Bekhouche SE (2017) Facial Soft biometrics: Extracting demographic traits. PhD thesis, Faculté des sciences et technologies

  6. Bekhouche SE, Dornaika F, Ouafi A, Taleb-Ahmed A (2017) Personality traits and job candidate screening via analyzing facial videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 10–13

  7. Bekhouche SE, Ouafi A, Dornaika F, Taleb-Ahmed A, Hadid A (2017) Pyramid multi-level features for facial demographic estimation. Expert Syst Appl 80:297–310

    Article  Google Scholar 

  8. Berry DS, McArthur LZ (1986) Perceiving character in faces: the impact of age-related craniofacial changes on social perception. Psychol Bull 100(1):3

    Article  Google Scholar 

  9. Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A (2018) Vggface2: A dataset for recognising faces across pose and age. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), IEEE, pp 67–74

  10. Dong Y, Lang C, Feng S (2019) General structured sparse learning for human facial age estimation. Multimed Syst 25(1):49–57

    Article  Google Scholar 

  11. Dornaika F, Arganda-Carreras I, Belver C (2019) Age estimation in facial images through transfer learning. Mach Vis Appl 30(1):177–187

    Article  Google Scholar 

  12. Dornaika F, Bekhouche S, Arganda-Carreras I (2020) Robust regression with deep cnns for facial age estimation: an empirical study. Expert Syst Appl 112942:141

    Google Scholar 

  13. Ebner NC, Riediger M, Lindenberger U (2010) Faces—a database of facial expressions in young, middle-aged, and older women and men: Development and validation. Behav Res Methods 42(1):351–362

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Gunay A, Nabiyev VV (2008) Automatic age classification with lbp. In: Computer and Information Sciences, 2008 ISCIS’08. 23rd International Symposium on, IEEE, pp 1–4

  16. Günay A, Nabiyev VV (2016) Age estimation based on hybrid features of facial images. In: Information Sciences and Systems 2015. Springer, New York, pp 295–304

  17. Günay A, Nabiyev VV (2017) Facial age estimation using spatial weber local descriptor. Int J Adv Telecommun Electrotechnics Signals Syst 6 (3):108–115

    Article  Google Scholar 

  18. Guo G, Mu G, Fu Y, Huang TS, Human age estimation using bio-inspired features (2009). In: Computer Vision and Pattern Recognition, 2009. CVPR IEEE Conference on, IEEE, 2009, pp 112–119

  19. Guo G, Wang X (2012) A study on human age estimation under facial expression changes. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, IEEE, pp 2547–2553

  20. Gurpinar F, Kaya H, Dibeklioglu H, Salah A (2016) Kernel elm and cnn based facial age estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 80–86

  21. 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, pp 770–778

  22. Hu Z, Wen Y, Wang J, Wang M, Hong R, Yan S (2016) Facial age estimation with age difference. IEEE Trans Image Process 26(7):3087–3097

    Article  MathSciNet  MATH  Google Scholar 

  23. Jain AK, Li SZ (2011) Handbook of face recognition, vol 1, Springer, New York

  24. Kannala J, Rahtu E (2012) Bsif: Binarized statistical image features. In: Pattern Recognition (ICPR), 2012 21st International Conference on, IEEE, pp 1363–1366

  25. Kazemi V, Sullivan J (2014) One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp 1867–1874

  26. King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755–1758

    Google Scholar 

  27. Kotowski K, Stapor K (2018) Deep learning features for face age estimation: Better than human?. In: International Conference: Beyond Databases, Architectures and Structures. Springer, New York, pp 376–389

  28. Kwon YH, da Vitoria Lobo N (1999) Age classification from facial images. Comput Vision Image Understand 74(1):1–21

    Article  Google Scholar 

  29. Lanitis A, Draganova C, Christodoulou C (2004) Comparing different classifiers for automatic age estimation. IEEE Trans Syst Man Cybernet Part B (Cybernetics) 34(1):621–628

    Article  Google Scholar 

  30. Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp 34–42

  31. Liao H (2019) Facial age feature extraction based on deep sparse representation. Multimed Tools Appl 78(2):2181–2197

    Article  Google Scholar 

  32. Liu H, Lu J, Feng J, Zhou J (2017) Group-aware deep feature learning for facial age estimation. Pattern Recogn 66:82–94

    Article  Google Scholar 

  33. Liu H, Lu J, Feng J, Zhou J (2017) Ordinal deep learning for facial age estimation. IEEE Trans Circ Syst Video Technol 29(2):486–501

    Article  Google Scholar 

  34. Liu H, Lu J, Feng J, Zhou J (2018) Label-sensitive deep metric learning for facial age estimation. IEEE Trans Inform Forensics Secur 13(2):292–305

    Article  Google Scholar 

  35. Liu X, Zou Y, Kuang H, Ma X (2020) Face image age estimation based on data augmentation and lightweight convolutional neural network. Symmetry 12 (1):146

    Article  Google Scholar 

  36. Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40(2):365–375

    Article  Google Scholar 

  37. Lu J, Liong VE, Zhou J (2015) Cost-sensitive local binary feature learning for facial age estimation. IEEE Trans Image Process 24(12):5356–5368

    Article  MathSciNet  MATH  Google Scholar 

  38. Minear M, Park DC (2004) A lifespan database of adult facial stimuli. Behav Res Methods Instruments Comput 36(4):630–633

    Article  Google Scholar 

  39. nadhir Zighem M-E, Ouafi A, Zitouni A, Ruichek Y, Taleb-Ahmed A (2019) Two-stages based facial demographic attributes combination for age estimation. J Vis Commun Image Represent 61:236–249

    Article  Google Scholar 

  40. Ojansivu V, Heikkilä J (2008) Blur insensitive texture classification using local phase quantization. In: International conference on image and signal processing. Springer, New York, pp 236–243

  41. Panis G, Lanitis A, Tsapatsoulis N, Cootes TF (2016) Overview of research on facial ageing using the fg-net ageing database. Iet Biometrics 5(2):37–46

    Article  Google Scholar 

  42. Parkhi OM, Vedaldi A, Zisserman A, et al. (2015) Deep face recognition. In: BMVC, vol 1, p 6

  43. Petra G (2013) Introduction to human age estimation using face images. J Slovak Univ Technol 21:24–30

    Google Scholar 

  44. Rothe R, Timofte R, Gool LV (2015) Dex: Deep expectation of apparent age from a single image. In: IEEE International Conference on Computer Vision Workshops (ICCVW)

  45. Sai P-K, Wang J-G, Teoh E-K (2015) Facial age range estimation with extreme learning machines. Neurocomputing 149:364–372

    Article  Google Scholar 

  46. Sawant M, Addepalli S, Bhurchandi K (2019) Age estimation using local direction and moment pattern (ldmp) features. Multimed Tools Appl 78 (21):30419–30441

    Article  Google Scholar 

  47. Sawant MM, Bhurchandi K (2019) Hierarchical facial age estimation using gaussian process regression. IEEE Access 7:9142–9152

    Article  Google Scholar 

  48. Shan C (2010) Learning local features for age estimation on real-life faces. In: Proceedings of the 1st ACM international workshop on Multimodal pervasive video analysis, ACM, pp 23–28

  49. Shen W, Guo Y, Wang Y, Zhao K, Wang B, Yuille A (2018) Deep regression forests for age estimation. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition

  50. Shen W, Guo Y, Wang Y, Zhao K, Wang B, Yuille AL (2019) Deep differentiable random forests for age estimation. IEEE Trans Pattern Anal Mach Intell

  51. Shu X, Tang J, Lai H, Liu L, Yan S (2015) Personalized age progression with aging dictionary. In: Proceedings of the IEEE international conference on computer vision, pp 3970–3978

  52. Shu X, Tang J, Lai H, Niu Z, Yan S (2016) Kinship-guided age progression. Pattern Recogn 59:156–167

    Article  Google Scholar 

  53. Shu X, Tang J, Liu L, Niu Z, Yan S (2015) What shall i look like after n years?. In: Proceedings of the 23rd ACM international conference on Multimedia, pp 789–790

  54. Shu X, Xie G-S, Li Z, Tang J (2016) Age progression: Current technologies and applications. Neurocomputing 208:249–261

    Article  Google Scholar 

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

  56. Tang J, Li Z, Lai H, Zhang L, Yan S, et al. (2017) Personalized age progression with bi-level aging dictionary learning. IEEE Trans Pattern Anal Mach Intell 40(4):905–917

    Google Scholar 

  57. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the IEEE Computer Society Conference on, volume 1, pages I–511–I–518 vol. 1, p 2001

  58. Yang H-F, Lin B-Y, Chang K-Y, Chen C-S (2018) Joint estimation of age and expression by combining scattering and convolutional networks. TOMCCAP 14(1):9–1

    Article  Google Scholar 

  59. Yang Z, Ai H (2007) Demographic classification with local binary patterns. In: International Conference on Biometrics. Springer, New York, pp 464–473

  60. Ylioinas J, Hadid A, Pietikäinen M (2012) Age classification in unconstrained conditions using lbp variants. In: Pattern recognition (icpr), 2012 21st international conference on, IEEE, pp 1257–1260

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, University of Djelfa, Djelfa, Algeria

    SE. Bekhouche

  2. University of the Basque Country UPV/EHU, San Sebastian, Spain

    SE. Bekhouche & F. Dornaika

  3. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    F. Dornaika

  4. Faculté des Nouvelles Technologies de l’information et de la communication, Laboratoire de Génie Électrique (LAGE), University of Kasdi Merbah Ouargla, Ouargla, 30000, Algeria

    A. Benlamoudi

  5. Laboratory of LESIA, University of Biskra, Biskra, Algeria

    A. Ouafi

  6. IEMN DOAE UMR CNRS 8520, UPHF, 59313, Valenciennes, France

    A. Taleb-Ahmed

Authors
  1. SE. Bekhouche
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. Dornaika
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Benlamoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Ouafi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. Taleb-Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F. Dornaika.

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

Bekhouche, S., Dornaika, F., Benlamoudi, A. et al. A comparative study of human facial age estimation: handcrafted features vs. deep features. Multimed Tools Appl 79, 26605–26622 (2020). https://doi.org/10.1007/s11042-020-09278-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09278-7

Keywords

Search

Navigation