Skip to main content

Advertisement

SpringerLink
Log in

Age and gender classification using Seg-Net based architecture and machine learning

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A facial recognition framework is a natural face-recognizing process from a computerized image or videos. Nowadays, for real-time applications, i.e., human–computer interaction, visual supervision, commercial applications, etc., Human Facial features are utilized for gender classification (GC) and age classification. This paper focuses on gender and age classification methodology from various face images—the proposed work based on Seg-Net-based architecture with machine learning algorithm gives excellent results. The overall accuracy increased through the advanced Seg-Net architecture and Support Vector Machine for age and gender recognition. Our proposed method achieved better results in Age classification on various datasets, i.e., Adience, IOG, and FG-Net datasets, accuracy 74.5%, 75.7%, and 92.48%, and in GC also achieved better results as compared to existing technology on the various datasets, i.e., Adience, IOG, FEI, and own datasets respectively accuracy 88.3%, 95.1%, 94.1%, and 91.8%.

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

References

  1. Ali ASO, Sagayan V, Saeed AM, Ameen H, Aziz A (2015) Age-invariant face recognition system using combined shape and texture features. IET Biom 4(2):98–115

    Article  Google Scholar 

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

  3. ANUSC Ozbulak G, Aytar Y, Ekenel HK (2016) How transferable are CNN-based features for age and gender classification? In: Biometrics Special Interest Group (BIOSIG), 2016 international conference of the IEEE, 2016, pp 1–6

  4. Bekhouche SE, Ouafi A, Benlamoudi A, Taleb-Ahmed A, Hadid A (2015) Facial age estimation and gender classification using multilevel local phase quantization. In: 2015 3rd International Conference on control, engineering & information technology (CEIT), IEEE, 2015, pp 1–4

  5. Bouchaffra D (2015) Nonlinear topological component analysis: application to age-invariant face recognition. IEEE Trans Neural Netw Learn Syst 26(7):1375–1387

    Article  MathSciNet  Google Scholar 

  6. Boyd S, Parikh N, Chu E, Peleato B, Eckstein J (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3:1–122

    Article  MATH  Google Scholar 

  7. Cascone L, Medaglia C, Nappi M, Narducci F (2020) Pupil size as a soft biometrics for age and gender classification. Pattern Recognit Lett 140(1):238–244

    Article  Google Scholar 

  8. Castrillón-Santana M, Lorenzo-Navarro J, Ramón-Balmaseda E (2015) Descriptors and regions of interest fusion for gender classification in the wild. arXiv preprint arXiv:1507.06838

  9. Castrillón-Santana M, Lorenzo-Navarro J, Ramón-Balmaseda E (2015) On using periocular biometric for gender classification in the wild. Pattern Recognit Lett 82:181–189

    Article  Google Scholar 

  10. CEPTED Chen J-C, Kumar A, Ranjan R, Patel VM, Alavi A, Chellappa R (2016) A cascaded convolutional neural network for age estimation of unconstrained faces. In: 2016 IEEE 8th international conference on biometrics theory, applications, and systems (BTAS), IEEE, 2016, pp 1–8

  11. Cerit B, Bölük SA, Demirci MF (2016) Analysis of the effect of image resolution on automatic face gender and age classification. In: 2016 24th Signal processing and communication application conference (SIU), pp 853–856. IEEE

  12. Chuan-xu W, Yun L, Zuo-Yong L (2008) Algorithm research of face image gender classification based on 2-D Gabor wavelet transform and SVM. International symposium on computer science and computational technology, 2008, vol 1, pp 312–315

  13. Coşğun S, Ozbek IY (2015) Age group classification and gender detection based on forced expiratory spirometry. 2015 IEEE

  14. Cottrell GW, Metcalfe J (1990) EMPATH: face, emotion, and gender recognition using holons. In: Advances in neural information processing systems. DBLP, pp 564–571

  15. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A 2(7):1160–1169

    Article  Google Scholar 

  16. Daugman J (1988) Theory of communication. Part 1: The analysis of information. IEEE Trans Acoust Speech Signal Process 36(7):1169–1179

    Article  MATH  Google Scholar 

  17. Donato G, Bartlett M, Hager J, Ekman P, Sejnowski T (1999) Classifying facial actions. IEEE Trans Pattern Anal Mach Intell (PAMI) 21(10):974–989

    Article  Google Scholar 

  18. Duan M, Li K, Yang C, Li K (2018) A deep hybrid learning CNN–ELM for age and gender classification. Neurocomputing 275:448–461

    Article  Google Scholar 

  19. Edelman B, Valentin DE, Abdi H (1998) Sex classification of face areas: how well can a linear neural network predict human performance? J Biol Syst 6:241–264

    Article  Google Scholar 

  20. EPTE Alnajar F, Shan C, Gevers T, Geusebroek J-M (2012) Learning-based encoding with a soft assignment for age estimation under unconstrained imaging conditions. Image Vis Comput 30:946–953

  21. Fazl-Ersi E, Mousa-Pasandi ME, Laganiere R, Awad M (2014) Age and gender recognition using informative features of various types. In: 2014 IEEE International conference on image processing (ICIP), IEEE, 2014, pp 5891–5895

  22. Fg-net. The Fg-net aging database. http://wwwprima.inrialpes.fr/FGnet/html/benchmarks.html. Accessed 19th March 2014

  23. Fukai H, Takimoto H, Mitsukura Y, Fukumi M (2007) Apparent age estimation system based on age perception. In: Proceedings of the SICE annual conference 2007, pp 2808–2812

  24. Gabay D, Mercier B (1976) A dual algorithm for the solution of nonlinear variational problems via finite element approximation. Comput Math Appl 2(1):17–40

    Article  MATH  Google Scholar 

  25. Gabor D (1946) Theory of communication. Part 1: The analysis of information. J Inst Electr Eng Part III Radio Commun Eng 93(26):429–441

    Google Scholar 

  26. Gallagher A, Chen T (2009) Understanding images of groups of people. In: Proceedings of CVPR, 2009

  27. Gao F, Ai H (2009) Face age classification on consumer images with Gabor feature and fuzzy LDA method. In: Proceedings of the international conference on advances in biometrics (ICB), 2009, pp 132–141

  28. Gawande MP, Agrawal DG (2014) Face recognition using PCA and different distance classifiers. IOSR J Electron Commun Eng (IOSR-JECE) 9(1):01–05

    Article  Google Scholar 

  29. Geng X, Zhou ZH, Zhang Y, Li G, Dai H (2006) Learning from facial aging patterns for automatic age estimation. ACM international conference on multimedia, Santa Barbara, CA, the USA, October, pp 307–316. DBLP

  30. Geng X, Zhou Z-H, Smith-Miles K (2007) Automatic age estimation based on facial aging patterns. IEEE Trans Pattern Anal Mach Intell 29(12):2234–2240

    Article  Google Scholar 

  31. Glowinski R, Marrocco A (1975) Sur l’approximation, par éléments finis d’ordre un, et la résolution, par pénalisation-dualité d’une classe de problèmes de Dirichlet non linéaires. ESAIM Modelisation Mathmatique et Analyse Numrique 9:41–76

    MATH  Google Scholar 

  32. Golomb BA, Lawrence DT, Sejnowski TJ (1991) SexNet: a neural network identifies sex from human faces. In: Advances in neural information processing systems. DBLP 1(2):572–579

  33. Gunay A, Nabiyev VV (2013) Automatic age classification with LBP. In: Proceedings of the 23rd international symposium on computer and information sciences, pp 1–4, October 2013

  34. Gutta S, Huang JRJ, Jonathon P et al (2000) Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Trans Neural Netw 11(4):948

    Article  Google Scholar 

  35. Hayashi JI, Yasumoto M, Ito H, Koshimizu H (2002). Age and gender estimation based on wrinkle texture and color of facial images. International conference on pattern recognition, 2002. Proceedings, vol 1, pp 405–408. IEEE

  36. Hosseini S, Lee SH, Kwon HJ, Koo HI, Cho NI (2018) Age and Gender Classification Using Wide Convolutional Neural Network and Gabor Filter. 2018 IEEE

  37. Hu M, Zheng Y, Ren F, Jiang H (2014) Age estimation and gender classification of facial images based on Local Directional Pattern. In: 2014 IEEE 3rd international conference on cloud computing and intelligence systems, pp 103–107. IEEE

  38. Iga R, Izumi K, Hayashi H, Fukano G, Ohtani T (2003) A gender and age estimation system from face images. In: Proceedings of the SICE annual conference, 2003, pp 756–761

  39. Jagtap J, Kokare M (2017) Human age classification using facial skin analysis and multi-class support vector machine. In: 2017 International conference on signal and information processing (IConSIP), pp 1–5

  40. Kecman V (2001) Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT Press

  41. Khan A, Majid A, Mirza AM (2005) Combination and optimization of classifiers in gender classification using genetic programming. Int J Knowl Based Intell Eng Syst 9(1):1–11

    Google Scholar 

  42. Kit FSD. Cognitec systems. http://www.cognitec-systems.de

  43. Ko JB, Lee W, Choi SE, Kim J (2014) A gender classification method using age information. In: 2014 International conference on electronics, information, and communications (ICEIC), pp 1–2. IEEE

  44. Zabala-Blanco D, Hernández-García R, Barrientos RJ, Mora M (2021) Evaluation of the standard and regularized ELMs for gender and age classification based on palm vein images. 2021 40th International Conference of the Chilean Computer Science Society (SCCC), p 1–8

  45. Altun H, Aksoy H (2021) A sequential iterative detection framework for gender and age classification. 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), p 1–6

  46. Hechmi K, Trong TN, Hautamäki V, Kinnunen T (2021) Voxceleb enrichment for age and gender recognition. 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), p 687–693

  47. Kale A, Altun O (2021) Age, gender and ethnicity classification from face images with CNN-based features. 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), p 1–6 https://doi.org/10.1007/s11277-018-5913-0

  48. Garain A, Ray B, Singh PK, Ahmadian A, Senu N, Sarkar R (2021) GRA_Net: a deep learning model for classification of age and gender from facial images. In IEEE Access 9:85672–85689 https://doi.org/10.1007/s11277-018-5923-y

  49. Xu C et al (2021) Real-time gait-based age estimation and gender classification from a single image. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), p 3459–3469

  50. Thepade SD, Dhake AR (2021) Fusion of Thepade SBTC and GLCM features for recognizing gender from facial images. 2021 International Conference on Communication information and Computing Technology (ICCICT), p 1–7

  51. Benkaddour MK, Lahlali S, Trabelsi M (2021) Human age and gender classification using convolutional neural network. 2020 2nd International Workshop on Human-Centric Smart Environments for Health and Well-being (IHSH), p 215–220

  52. Kwon YH, Vitoria Lobo ND (1999) Age classification from facial images. Comput Vis Image Underst 74(1):1–21

    Article  Google Scholar 

  53. Lades M, Vorbruggen J, Buhmann J, Lange J, von der Malsburg C, Wurtz R, Konen W (1993) Distortion invariant object recognition in the dynamic link architecture. IEEE Trans Comput 42(3):300–311

    Article  Google Scholar 

  54. Li Z, Park U, Jain AK (2011) A discriminative model for age-invariant face recognition. IEEE Trans Inf Forensics Secur 6(3):1028–1037

    Article  Google Scholar 

  55. Liu C, Wechsler H. Independent component analysis of Gabor features for face recognition. IEEE

  56. Liu X, Li J, Hu C, Pan J-S (2017) Deep convolutional neural networks-based and gender classification with facial images. IEEE

  57. Ma D, Rothe R, Timofte R, Van Gool L (2016) Deep expectation of real and parentage from a single image without facial landmarks. Int J Comput Vis 126(2):1–14

    Google Scholar 

  58. Mery D, Bowyer K (2014) Recognition of facial attributes using adaptive sparse representations of random patches. In: ECCV Workshops, Springer, pp 778–792

  59. Nayak JS, Indiramma M (2021) An approach to enhance age invariant face recognition performance based on gender classification. J King Saud Univ Comput Inf Sci 1(1):1–9

    Google Scholar 

  60. Park U, Tong Y, Jain AK (2010) Age-invariant face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):947–954

    Article  Google Scholar 

  61. Peng L (ID: 03090345). Adaptive median filtering. 140.429 digital image processing

  62. Phillips P, Moon H, Rizvi S, Rauss P (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell (PAMI) 22(10):1090–1104

    Article  Google Scholar 

  63. Reddy PCS, Sarma KSRK, Sharma A, Rao PV, Rao SG, Sakthidharan GR, Kavitha K (2020) Enhanced age prediction and gender classification (EAP-GC) framework using regression and SVM techniques. Mater Today Proc 2:1–8

    Google Scholar 

  64. Ricanek K Jr, Mahalingam G, Albert AM, Vorder Bruegge RW. Human face aging: a prospective analysis from anthropometry and biometrics. Book Chapter in Age factors in biometric processing edited by Michael Fairhurst

  65. RIPT Levi G, Hassncer T (2015) Age and gender classification using convolution neural networks. In: CVPR Workshops, 2015, pp 34–42

  66. RIPT Mansanet J, Albiol A, Paredes R (2016) Local deep neural networks gender recognition. Pattern Recognit Lett 70:80–86

  67. Sakarkaya M, Yanbol F, Kurt Z (2012) Comparison of several classification algorithms for gender recognition from face images. The IEEE 16th international conference on intelligent engineering systems (INES), pp 97–101

  68. Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell (PAMI) 25(12):1615–1618

    Article  Google Scholar 

  69. Srikrishnaswetha K, Kumar S, Johri P (2018) Comparision study on various face detection techniques. In: 2018 4th international conference on computing communication and automation (ICCCA), pp 1–5

  70. Srikrishnaswetha K, Kumar S, Mahmood MdR (2019) A study on smart electronics voting machine using face recognition and Aadhar verification with iot. In: Innovations in electronics and communication engineering, pp 87–95

  71. Srikrishnaswetha K, Kumar S, Ghai D (2020) Secured electronic voting machine using biometric technique with unique identity number and iot. In Saini HS, Singh RK, Mirza Tariq Beg, Sahambi JS (eds) Innovations in electronics and communication engineering. Springer, pp 311–326

  72. Sun Z, Bebis G, Yuan X, Louis SJ (2002) Genetic feature subset selection for gender classification: a comparison study. In: Proceedings of 6th IEEE Workshop on applications of computer vision, pp 165–170

  73. Suo J, Zhu S-C, Shan S, Chen X (2010) A compositional and dynamic model for facial aging. IEEE Trans Pattern Anal Mach Intell 32(3):385–401

    Article  Google Scholar 

  74. Takimoto H, Mitsukura Y, Fukumi M, Akamatsu N (2006) A design of gender and age estimation system based on facial knowledge. In: Proceedings of the SICE-ICASE international joint conference, 2006, pp 3883–3886

  75. Tapia JE, Perez CA (2013) Gender classification based on the fusion of different spatial scale features selected by mutual information from the histogram of LBP, intensity, and shape. IEEE Trans Inf Forensics Secur 8:488–499

    Article  Google Scholar 

  76. The Face databases website. http://www.face-rec.org/databases/

  77. Txia J-D, Huang C-L (2009) Age estimation using AAM and local facial features. 2009 Fifth international conference on intelligent information hiding and multimedia signal processing, pp 885–888

  78. van de Wolfshaar J, Karaaba MF, Wiering MA (2015) Deep convolutional neural networks and support vector machines for gender

  79. Vapnik V (1995) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  80. Wu S, Wang D (2019) Effect of the subject’s age and gender on face recognition results. J Vis Commun Image Represent 60:116–122

    Article  Google Scholar 

  81. Xiao B, Yang X, Xu Y, Zha H (2009) Learning distance metric for regression by semidefinite programming with application to human age estimation. In: Proceedings of the 17th ACM international conference on multimedia, 2009, pp 451–460

  82. Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, Zemel RS, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. Preprint arXiv:1502.03044

  83. Yang Z, Ai H (2012) Demographic classification with local binary patterns. In: Proceedings of the international conference on advances in biometrics (ICB), pp 464–473

  84. Zhang D, Wang Y-H (2008) Gender recognition based on the fusion of face and gait information. The international conference on machine learning and cybernetics, pp 62–67

  85. Zhou SK, Georgescu B, Zhou X, Comaniciu D (2010). Method for performing image-based regression using boosting. US,US7804999

Download references

Funding

Funding information is not applicable/No funding was received.

Author information

Authors and Affiliations

  1. ECE Department, Punjab Engineering College, Chandigarh, 160012, India

    Sandeep Kumar & Sukhwinder Singh

  2. EE Department, Punjab Engineering College, Chandigarh, 160012, India

    Jagdish Kumar

  3. E&TC Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, 412115, India

    K. M. V. V. Prasad

Authors
  1. Sandeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sukhwinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jagdish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. K. M. V. V. Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sandeep Kumar.

Ethics declarations

Conflict of interest

Sandeep Kumar is currently working as a Research Scholar at PEC University of Technology, Chandigarh. Sandeep Kumar declares that he has no conflict of interest. Dr. Sukhwinder Singh is presently working as an Assistant professor at PEC University of Technology, Chandigarh. Dr. Sukhwinder Singh declares that he has no conflict of interest. Jagdish Kumar is currently working as a Professor at PEC University of Technology, Chandigarh. Dr. Jagdish Kumar states that he has no conflict of interest. Dr. KMVV Prasad is presently working as an Associate Professor, Department of E&TC, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India and he has no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants followed the institutional or national research committee's ethical standards.

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

Kumar, S., Singh, S., Kumar, J. et al. Age and gender classification using Seg-Net based architecture and machine learning. Multimed Tools Appl 81, 42285–42308 (2022). https://doi.org/10.1007/s11042-021-11499-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11499-3

Keywords

Search

Navigation