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Gender effect on age classification in an unconstrained environment

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Abstract

The face aging process is subject to multiple influences. This may probably involve several inherited and various environmental and biological factors like the differences observed between males and females. In fact, male and female facial skin differs as far as the type, the consistency and the sensitivity to external factors is concerned. In this paper, we proposed a new age classification method that consists of classifying human faces into various age groups by exploring the correlation between age and gender information. Moreover, we suggested a two-level age classification to reduce confusion between age groups. Our experiments were conducted on the Adience benchmark and the FG-Net dataset for the age classification and on the Groups and FERET datasets for the gender estimation. The experimental results reveal the good performance of our method while identifying the age groups in challenging contexts.

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References

  1. Smulyan H, Asmar RG, Rudnicki A, London GM, Safar ME (2001) Comparative effects of aging in men and women on the properties of the arterial tree. Journal of the American College of Cardiology 37(5):1374–1380

    Article  Google Scholar 

  2. Sveikata, K., Balciuniene, I., and Tutkuviene, J. Factors influencing face aging. literature review. Stomatologija 13, 4 (2011), 113–116

  3. Eidinger E, Enbar R, Hassner T (2014) Age and gender estimation of unfiltered faces. IEEE Transactions on Information Forensics and Security 9(12):2170–2179

    Article  Google Scholar 

  4. Jagtap J, Kokare M (2016) Human age classification using facial skin aging features and artificial neural network. Cognitive Systems Research 40:116–128

    Article  Google Scholar 

  5. Liu K-H, Liu T-J (2019) A structure-based human facial age estimation framework under a constrained condition. IEEE Transactions on Image Processing 28(10):5187–5200

    Article  MathSciNet  Google Scholar 

  6. Xia Z, Hong X, Gao X, Feng X, Zhao G (2019) Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions. IEEE Transactions on Multimedia 22(3):626–640

    Article  Google Scholar 

  7. Jung M, Chi S (2020) Human activity classification based on sound recognition and residual convolutional neural network. Automation in Construction 114:103–177

    Article  Google Scholar 

  8. Mliki H, Dammak S, Fendri E (2020) An improved multi-scale face detection using convolutional neural network. Signal, Image and Video Processing 14(7):1345–1353

    Article  Google Scholar 

  9. Yuan J, Xiong H-C, Xiao Y, Guan W, Wang M, Hong R, Li Z-Y (2020) Gated cnn: Integrating multi-scale feature layers for object detection. Pattern Recognition 105:107–131

    Article  Google Scholar 

  10. Ramchandran A, Sangaiah AK (2019) Unsupervised deep learning system for local anomaly event detection in crowded scenes. Multimedia Tools and Applications 1–21

  11. Minaee S, Boykov YY, Porikli F, Plaza AJ, Kehtarnavaz N (2021) and Terzopoulos, D. A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Image segmentation using deep learning

    Google Scholar 

  12. Tajbakhsh N, Jeyaseelan L, Li Q, Chiang JN, Wu Z, Ding X (2020) Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation. Medical Image Analysis 63:93–101

    Article  Google Scholar 

  13. Vijayan M, Mohan R (2020) A universal foreground segmentation technique using deep-neural network. Multimedia Tools and Applications 79(47):34835–34850

    Article  Google Scholar 

  14. Chen L, Fan C, Yang H, Hu S, Zou L, Deng D (2018) Face age classification based on a deep hybrid model. Signal, Image and Video Processing 12(8):1531–1539

    Article  Google Scholar 

  15. Ng, C.-B., and Lo, W.-H. Effect of image distortion on facial age and gender classification performance of convolutional neural networks. In IOP Conference Series: Materials Science and Engineering (2019), vol. 495, IOP Publishing, pp. 012–029

  16. Duan M, Li K, Yang C, Li K (2018) A hybrid deep learning cnn-elm for age and gender classification. Neurocomputing 275:448–461

    Article  Google Scholar 

  17. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern recognition 29(1):51–59

    Article  Google Scholar 

  18. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press

  19. Báez-Suárez, A., Nikou, C., Nolazco-Flores, J. A., and Kakadiaris, I. A. Age classification from facial images: Is frontalization necessary? In International Symposium on Visual Computing (2016), Springer, pp. 769–778

  20. Ling, H., Soatto, S., Ramanathan, N., and Jacobs, D. W. A study of face recognition as people age. In 2007 IEEE 11th International Conference on Computer Vision (2007), IEEE, pp. 1–8

  21. Guo, G., Mu, G., Fu, Y., and Huang, T. S. Human age estimation using bio-inspired features. In 2009 IEEE conference on computer vision and pattern recognition (2009), IEEE, pp. 112–119

  22. Webb AR (2003) Statistical pattern recognition. John Wiley & Sons

  23. Agbo-Ajala O, Viriri S (2020) Deep learning approach for facial age classification: a survey of the state-of-the-art. Artificial Intelligence Review 1–35

  24. Zhang C, Ding H, Shang Y, Shao Z, Fu X (2018) Gender classification based on multiscale facial fusion feature. Mathematical Problems in Engineering 2018:

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

  26. Dwivedi, N., and Singh, D. K. Review of deep learning techniques for gender classification in images. In Harmony Search and Nature Inspired Optimization Algorithms. Springer, 2019, pp. 1089–1099

  27. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 1097–1105

  28. Aslam A, Hussain B, Cetin AE, Umar AI, Ansari R (2018) Gender classification based on isolated facial features and foggy faces using jointly trained deep convolutional neural network. Journal of Electronic Imaging 27(5):023–053

    Article  Google Scholar 

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

  30. Aslam A, Hayat K, Umar AI, Zohuri B, Zarkesh-Ha P, Modissette D, Khan SZ, Hussian B (2019) Wavelet-based convolutional neural networks for gender classification. Journal of Electronic Imaging 28(1):1–12

    Article  Google Scholar 

  31. Sidney Burrus C, Gopinath RA, Guo H (1998) Introduction to wavelets and wavelet transforms. Upper Saddle River, NJ, USA, A Primer; Prentice Hall

    Google Scholar 

  32. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Article  Google Scholar 

  33. Levi G, Hassner T (2015) Age and gender classification using convolutional neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition workshops 34–42

  34. Mallat S (2012) Group invariant scattering. Communications on Pure and Applied Mathematics 65(10):1331–1398

    Article  MathSciNet  Google Scholar 

  35. Zhang, L., Chu, R., Xiang, S., Liao, S., and Li, S. Z. Face detection based on multi-block lbp representation. In International conference on biometrics (2007), Springer, pp. 11–18

  36. Dagher I, Azar F (2019) Improving the svm gender classification accuracy using clustering and incremental learning. Expert Systems 36(3):1–17

    Article  Google Scholar 

  37. Huang, G. B., Mattar, M., Berg, T., and Learned-Miller, E. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on Faces in ’Real-Life’ Images: Detection, Alignment, and Recognition (2008)

  38. Wolf, L., Hassner, T., and Maoz, I. Face recognition in unconstrained videos with matched background similarity. In CVPR 2011 (2011), IEEE, pp. 529–534

  39. Ruder, S. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)

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

  41. Gallagher, A. C., and Chen, T. Understanding images of groups of people. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (2009), IEEE, pp. 256–263

  42. Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image and vision computing 16(5):295–306

    Article  Google Scholar 

  43. Afifi M, Abdelhamed A (2019) Afif4: deep gender classification based on adaboost-based fusion of isolated facial features and foggy faces. Journal of Visual Communication and Image Representation 62:77–86

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Economics and Management of Sfax, MIRACL-FSEG, University of Sfax, Road Airport Km 4, Sfax, 3018, Tunisia

    Sahar Dammak

  2. National School of Electronics and Telecommunications of Sfax, MIRACL-FSEG, University of Sfax, Road Tunis city El Ons, Sfax, 3018, Tunisia

    Hazar Mliki

  3. Faculty of Sciences of Sfax, MIRACL-FSEG, University of Sfax, Road Sokra Km 3, Sfax, 3018, Tunisia

    Emna Fendri

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  1. Sahar Dammak
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  2. Hazar Mliki
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  3. Emna Fendri
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Correspondence to Sahar Dammak.

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Dammak, S., Mliki, H. & Fendri, E. Gender effect on age classification in an unconstrained environment. Multimed Tools Appl 80, 28001–28014 (2021). https://doi.org/10.1007/s11042-021-11060-2

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  • DOI: https://doi.org/10.1007/s11042-021-11060-2

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