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Face-Based Age and Gender Estimation Using Improved Convolutional Neural Network Approach

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Abstract

Age and gender estimation using face images by working on the facial features of the human, which are unique for each person can serve several applications such as person’s identification, access control, human–machine interaction, to avoid any type of fraud or misuse of someone’s identity, forensic, and in organizations. However, previous work for age estimation was based on handcraft features to encode the ageing patterns. With the advancement of deep architectures in CNN algorithms, CNN shows better performance than handcraft features. There exist several methods and relatively substantial literature on the field. However, biological variations and uncertainty will always be associated with age estimates because of the large variety in facial appearance and several other extrinsic and intrinsic factors. Thus, the proposed work is based on age and gender estimation using an improved convolutional neural network (CNN). The proposed model improves the computational performance, and it is generalized to another dataset also. The convolutional layer is the fundamental building block of a CNN. Like other neural networks, CNNs consist of neurons that are capable of learnable weights and biases. The proposed model is evaluated using UTKFace, IMDB-WIKI, FG-NET, CACD datasets. The efficacy of the proposed algorithm in comparison to existing algorithms achieves better accuracy in age estimation through extensive simulations. The proposed model is trained on UTKFace (aligned and cropped faces) dataset has shown 94.01% accuracy for age and 99.86% accuracy for gender estimation.

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References

  1. https://www.lifelock.com/learn-identity-theft-resources-identity-theft-vs-identity-fraud-whats-the-difference.html

  2. Timme, M., Steinacker, J. M., & Schmeling, A. (2016). Age estimation in competitive sports. International Journal of Legal Medicine, 131(1), 225–233.

    Article  Google Scholar 

  3. Wang, Q., Lai, J., Claesen, L., et al. (2020). A novel feature representation: Aggregating convolution kernels for image retrieval. Neural Networks, 130, 1–10. https://doi.org/10.1016/j.neunet.2020.06.010

    Article  Google Scholar 

  4. Siegel, J. W., & Xu, J. (2020). Approximation rates for neural networks with general activation functions. Neural Networks, 128, 313–321. https://doi.org/10.1016/j.neunet.2020.05.019

    Article  MATH  Google Scholar 

  5. Majumdar, A. (2020). Graph transform learning. Neural Networks, 128, 248–253. https://doi.org/10.1016/j.neunet.2020.05.020

    Article  MATH  Google Scholar 

  6. Kortli, Y., Jridi, M., Falou, A. I. A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342. https://doi.org/10.3390/s20020342

    Article  Google Scholar 

  7. Khan, S., Akram, A., & Usman, N. (2020). Real-time automatic system for face recognition using face API and OpenCV. Wireless Personal Communications, 113(1), 469–480. https://doi.org/10.1007/s11277-020-07224-2

    Article  Google Scholar 

  8. Moustafa, A. A., Elnakib, A., & Areed, N. F. F. (2020). Age-invariant face recognition based on deep features analysis. Signal, Image and Video Processing, 14, 1027–1034. https://doi.org/10.1007/s11760-020-01635-1

    Article  Google Scholar 

  9. Atallah, R. R., Kamsin, A., Ismail, M. A., Abdelrahman, S. A., & Zerdoumi, S. (2018). Face recognition and age estimation implications of changes in facial features: A critical review study. IEEE Access, 6, 28290–28304.

    Article  Google Scholar 

  10. Levi, G., & Hassner, T. (2015). Age and gender classification using convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 34–42.

  11. Yan, C., Lang, C., Wang, T., Du, X., & Zhang, C. (2014). Age Estimation Based on Convolutional Neural Network. Pacific Rim Conference on Multimedia (pp. 211–220). Springer.

  12. Savchenko, A. V. (2019). Efficient facial representation for age, gender and identity recognition in organizing photo albums using multi-output CNN. PeerJ Computer Science, 5, e197.

    Article  Google Scholar 

  13. Kaur, H., & Jindal, N. (2020). Image and video forensics: A critical survey. Wireless Personal Communications, 112, 1281–1302. https://doi.org/10.1007/s11277-020-07102-x

    Article  Google Scholar 

  14. Duta I. C., Liu, L., Zhu, F., & Shao, L. (2020). Pyramidal convolution: Rethinking convolutional neural network for visual recognition. arXiv:2006.11538v1. https://arxiv.org/pdf/2006.11538.pdf.

  15. Bahng, H., Chun, S., Yun, S., Choo, J., & Oh, S. J. (2020). Learning De-biased Representation with Biased Representation. In International Conference on Machine Learning, 528–539. PMLR.

  16. Wang, Z. J., Turko, R., Shaikh, O., et al. (2020). CNN explainer: Learning convolutional neural networks with interactive visualization. IEEE Transactions on Visualization and Computer Graphics, 27(2), 1396–1406.

    Article  Google Scholar 

  17. Debgupta, R., Tripathy, B.K., & Chaudhuri, B.B. (2020). A Wide ResNet-based approach for age and gender estimation in face images. International Conference on Innovative Computing and Communications, 517–530. Springer, Singapore.

  18. Ciresan, D. C., Meier, U., Gambaredella, L. M., & Schmidhuber, J. (2010). Deep big simple neural nets for handwritten digit recognition. Neural Computation, 22(12), 3207–3220.

    Article  Google Scholar 

  19. 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, 770–778.

  20. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural network. Advances in Neural Information Processing Systems, 25, 1097–1105.

    Google Scholar 

  21. Alzubaidi, L., Zhang, J., Humadi, A. J., et al. (2021). Review of deep learning: Concepts, CNN architecture, challenges, applications, future directions. Journal of Big Data, 8, 53. https://doi.org/10.1186/s40537-021-00444-8

    Article  Google Scholar 

  22. Kwon, Y. H., & da Vitoria Lobo, N. (1999). Age Classification from Facial Images. Computer Vision and Image Understanding Journal, 74(1), 1–21.

    Article  Google Scholar 

  23. Lanitis, A., Taylor, C. J., & Cootes, T. F. (2002). Toward automatic simulation of aging effects on face images. IEEE Transactions of Pattern Analysis and Machine Intelligence, 24(4), 442–455.

    Article  Google Scholar 

  24. Geng, X., Zhou, Z. H., & Smith-Miles, K. (2007). Automatic age estimation based on facial aging patterns. IEEE Transactions of Pattern Analysis and Machine Intelligence, 29(12), 2234–2240.

    Article  Google Scholar 

  25. Guo, G., Fu, Y., Dyer, C. R., & Huang, T. S. (2008). Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing, 17(7), 1178–1188.

    Article  MathSciNet  Google Scholar 

  26. Wang, J.G., Yau, W.Y., & Wang, H. L. (2009). Age categorization via ECOC with fused Gabor and LBP Features. In 2009 Workshop on Applications of Computer Vision (WACV), 1–6. https://doi.org/10.1109/WACV.2009.5403085.

  27. Han, H., Otto, C., & Jain, A.K. (2013). Age estimation from face images: Human vs. machine performance. In 2013 international conference on biometrics (ICB),1–8. IEEE.

  28. Huerta, I., Fernández, C., & Prati, A. (2014). Facial age estimation through the fusion of texture and local appearance descriptors. European Conference on Computer Vision (pp. 667–681). Springer.

  29. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1–9. https://doi.org/10.1109/CVPR.2015.7298594.

  30. Zhang, K., Sun, M., Han, T. X., Yuan, X., Guo, L., & Liu, T. (2018). Residual networks of residual networks: Multilevel residual networks. IEEE Transactions on Circuits and Systems for Video Technology, 28(6), 1303–1314.

    Article  Google Scholar 

  31. Zhang, K., Guo, L., Gao, C., & Zhao, Z. (2017). Pyramidal RoR for image classification. Cluster Computing, 22, 5115–5125. https://doi.org/10.1007/s10586-017-1443-x

    Article  Google Scholar 

  32. Selim, M., Raheja, S., & Stricker, D. (2015). Real-time human age estimation based on Facial images using uniform Local Binary Patterns. In VISAPP (2).

  33. Karthigayani, P., & Sridhar, S. (2013). Detection tree-based occlusion detection in face recognition and estimation of human age using back propagation neural network. Journal of Computer Science and Technology, 10(1), 115–127. https://doi.org/10.3844/jcssp.2014.115.127

    Article  Google Scholar 

  34. Zhang, K., Gao, C., Guo, L., Sun, M., Yuan, X., Han, T. X., Zhao, Z., & Li, B. (2017). Age group and gender estimation in the wild with deep RoR architecture. IEEE Access, 5, 22492–22503.

    Article  Google Scholar 

  35. Gao, B. B., Xing, C., Xie, C. W., Wu, J., & Geng, X. (2017). Deep label distribution learning with label ambiguity. IEEE Transactions on Image Processing, 26(6), 2825–2838.

    Article  MathSciNet  Google Scholar 

  36. Abbas, A. R., & Kareem, A. R. (2018). Intelligent age estimation from facial images using machine learning techniques. Iraqi Journal of Science, 724–732.

  37. Liu, K.H., Liu, H. H., Pei, S. C. et al. (2019). Age estimation on low quality face images. IEEE international conference on artificial intelligence circuits and systems (AICAS), 295–296. IEEE.

  38. Agbo-Ajala, O., & Viriri, S. (2020). Deeply learned classifiers for age and gender predictions of unfiltered faces. The Scientific World Journal, 2020, Article ID 1289408.

  39. Yang, H. F., Lin, B.Y., Chang, K.Y., & Chen, C.S. (2013). Automatic age estimation from face images via deep ranking networks. 35(8), 1872–1886.

  40. Yi, D., Lei, Z., & Li, S. Z. (2014). Age estimation by multi-scale convolutional network. In Asian Conference on Computer Vision, 144–158. Sprigger, Cham.

  41. Wang, X., Guo, R., & Kambhamettu, C. (2015). Deeply-learned feature for age estimation. In 2015 IEEE winter conference on applications of computer vision, 534–541. IEEE.

  42. Rothe, R., Timofte, R., & Gool, L.V. (2016). Deep Expectation of real and apparent age from single image without facial landmarks. International Journals of Computer Vision, 144–157.

  43. Antipov, G., Baccouche, M., Berrani, S. A., & Dugelay, J. L. (2017). Effective training of convolutional neural network for face-based gender and age prediction. Pattern Recognition, 72, 15–26.

    Article  Google Scholar 

  44. 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, 5285–5294.

  45. Ouafi, A., Zitouni, A., Ruichek, Y., & Taleb-Ahmed, A. (2019). Two-stages based facial demographic attributes combination for age estimation. Journal of Visual Communication and Image Representation., 61, 236–249.

    Article  Google Scholar 

  46. 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. https://doi.org/10.3390/sym12010146.

  47. Hiba, S., Keller, Y. (2021). Hierarchical attention-based age estimation and Bias estimation. arXiv:2103.09882v1. https://arxiv.org/pdf/2103.09882.pdf.

  48. Xu, C., Makihara, Y., Liao, R., Niitsuma, H., Li, X., Yagi, Y., & Lu, J. (2021). Real-time gait-based age estimation and gender classification from a single image. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 3460–3470. https://doi.org/10.1109/WACV48630.2021.00350.

  49. Greco, A., Saggese, A., Vento, M., & Vigilante, V. (2021). Effective training of convolutional neural networks for age estimation based on Knowledge distillation. Neural Computing and Applications. https://doi.org/10.1007/s00521-021-05981-0

    Article  Google Scholar 

  50. Wang, H., Wei, X., Sanchez, V., & Li, C-T. (2018). Fusion network for face-based age estimation. In 2018 25th IEEE International Conference on Image Processing (ICIP) (pp. 2675–2679). IEEE. https://doi.org/10.1109/ICIP.2018.8451606.

  51. Fariza, A., Arifin, A.Z. (2019). Age estimation system using deep residual network classification method. In 2019 International Electronics Symposium (IES) (pp. 607–611). IEEE.

  52. Cao, W., Mirjalili, V., & Raschka, S. (2019). Rank-consistent ordinal regression for neural networks. 1(6), 13. arXiv:1901.07884v4. https://arxiv.org/pdf/1901.07884.pdf.

  53. Savov, N., Ngo, M., Karaoglu, S., Dibeklioglu, H., & Gevers, T. (2020). Pose and expression robust age estimation via 3d face reconstruction from a single image. In Proceedings of the IEEE/CVF international conference on computer vision workshops. 1270–1278. https://doi.org/10.1109/ICCVW.2019.00160.

  54. Sendik, O., & Keller, Y. (2019). Deep age: Deep learning of face-based age estimation. Signal Processing: Image Communication, 78, 368–375.

    Google Scholar 

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Authors and Affiliations

  1. Punjabi University, Patiala, Punjab, India

    Neha Sharma & Reecha Sharma

  2. Assistant Professor (Electronics and Communication), Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Neeru Jindal

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  1. Neha Sharma
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Sharma, N., Sharma, R. & Jindal, N. Face-Based Age and Gender Estimation Using Improved Convolutional Neural Network Approach. Wireless Pers Commun 124, 3035–3054 (2022). https://doi.org/10.1007/s11277-022-09501-8

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