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Human Age Recognition Method Based on Facial Images Using an Ensemble of Neural Network Classifiers

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Information Technology for Education, Science, and Technics (ITEST 2024)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 222))

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Abstract

This paper proposes a method for recognizing the age of a person from facial images by using an ensemble of neural network classifiers. The objective of this research is to enhance the effectiveness of human age recognition by employing a neural network classifiers ensemble. The developed method has several advantages: the input image is not required to be square, thereby expanding its applicability; the “convolutional layer – pooling layer” pairs count is calculated empirically, enhancing the neural network classifier accuracy; the count of layers’ planes is calculated automatically, speeding up the determination of the neural network classifier’s structure; the use of an ensemble of neural network classifiers allows for high probability age range classification of individuals. Compared to the scikit-learn package, the proposed method can explore classification using an ensemble of ANN and Hidden Markov Models rather than just an ensemble of neural networks. In addition, this method can explore classification using an ensemble of ANN and Hidden Markov Models rather than only just Hidden Markov Models when compared to the hmmlearn package. Future research prospects include the utilization of the proposed ensemble neural network image recognition method for various intelligent systems aimed at recognizing human characteristics such as gender and emotions.

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References

  1. Pan, H., Han, H., Shan, S., Chen, X.: Mean-variance loss for deep age estimation from a face. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5285–5294. Salt Lake City, UT (2018)

    Google Scholar 

  2. Zhang K., et al.: Fine-grained age estimation in the wild with attention LSTM networks. In: IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 9, pp. 3140–3152 (2020). https://doi.org/10.48550/arXiv.1805.10445

  3. Kumar, B.A., Misra, N.K.: Masked face age and gender identification using CAFFE-modified MobileNetV2 on photo and real-time video images by transfer learning and deep learning techniques. Expert Syst. Appl. 246, 1–25 (2024)

    Article  Google Scholar 

  4. Alonso-Fernandez, F., Hernandez-Diaz, K., Ramis, S., Perales, F.J., Bigun, J.: Facial masks and soft-biometrics: Leveraging face recognition CNNs for age and gender prediction on mobile ocular images. IET Biometrics 10(5), 562–580 (2021). https://doi.org/10.1049/bme2.12046

    Article  Google Scholar 

  5. Wang, H., et al.: CosFace: Large margin cosine loss for deep face recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5265–5274. Salt Lake City, UT, USA (2018). https://doi.org/10.1109/CVPR.2018.00552

  6. Bennetts, R.J., Johnson, H.P., Zielinska, P., Bate, S.: Face masks versus sunglasses: limited effects of time and individual differences in the ability to judge facial identity and social traits. Cogn. Res. 7(1), 1–24 (2022)

    Article  Google Scholar 

  7. Neskorodieva, T., Fedorov, E.: Neural Network models ensembles for generalized analysis of audit data transformations. In: Shkarlet, S., Morozov, A., Palagin, A., Vinnikov, D., Stoianov, N., Zhelezniak, M., Kazymyr, V. (eds.) Mathematical Modeling and Simulation of Systems: Selected Papers of 16th International Scientific-practical Conference, MODS, 2021 June 28–July 01, Chernihiv, Ukraine, pp. 263–279. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-030-89902-8_21

    Chapter  Google Scholar 

  8. Liu, L., Lin, B., Yang, Y.: Moving scene object tracking method based on deep convolutional neural network. Alex. Eng. J. 86, 592–602 (2024)

    Article  Google Scholar 

  9. Kang, K., et al.: T-CNN: tubelets with convolutional neural networks for object detection from videos. IEEE Trans. Cir. Syst. Video Technol. 28(10), 2896–2907 (2018). https://doi.org/10.1109/TCSVT.2017.2736553

    Article  Google Scholar 

  10. Solovyev, R., Wang, W., Gabruseva, T.: Weighted boxes fusion: ensembling boxes from different object detection models. Image Vis. Comput. 107, 104117 (2021). https://doi.org/10.1016/j.imavis.2021.104117

    Article  Google Scholar 

  11. Wan, L., Chen, Y., Li, H., Li, C.: Rolling-element bearing fault diagnosis using improved LeNet-5 network. Sensors 20, 1693 (2020). https://doi.org/10.3390/s20061693

    Article  Google Scholar 

  12. Ouyang, X., et al.: A 3D-CNN and LSTM based multi-task learning architecture for action recognition. IEEE Access 7, 40757–40770 (2019). https://doi.org/10.1109/ACCESS.2019.2906654

    Article  Google Scholar 

  13. Kim, T.Y., Cho, S.: Predicting residential energy consumption using CNN-LSTM neural networks. Energy 182(5), 72–81 (2019). https://doi.org/10.1016/j.energy.2019.05.230

    Article  Google Scholar 

  14. Yang, R., et al.: CNN-LSTM deep learning architecture for computer vision-based modal frequency detection. Mech. Syst. Signal Process. 144, 106885 (2020). https://doi.org/10.1016/j.ymssp.2020.106885

    Article  Google Scholar 

  15. Kumar, A., Zhang, Z.J., Lyu, H.: Object detection in real time based on improved single shot multi-box detector algorithm. J. Wirel. Commun. Netw. 2020, 204 (2020). https://doi.org/10.1186/s13638-020-01826-x

    Article  Google Scholar 

  16. Tang, W., Sun, J., Wang, S., Zhang, Y.: Review of AlexNet for medical image classification. ArXiv, abs/2311.08655 (2023). https://doi.org/10.48550/arXiv.2311.08655

  17. Kumar, G.S.C., Kumar, R.K., Kumar, K.P.V., Sai, N.R., Brahmaiah, M.: Deep residual convolutional neural network: an efficient technique for intrusion detection system. Expert Syst. Appl. 238, 121912 (2024). https://doi.org/10.1016/j.eswa.2023.121912

    Article  Google Scholar 

  18. Wang, S., et al.: Single and simultaneous fault diagnosis of gearbox via wavelet transform and improved deep residual network under imbalanced data. Eng. Appl. Artif. Intell. 133, 108146 (2024). https://doi.org/10.1016/j.engappai.2024.108146

    Article  Google Scholar 

  19. de Lima, J.P.C., Khan, A.A., Carro, L., Castrillon, J.: Full-stack optimization for cam-only DNN inference (2024). https://doi.org/10.48550/arXiv.2401.12630

  20. Novitasari, D.C., et al.: Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network. Commun. Math. Biol. Neurosci. 42 (2020). https://doi.org/10.28919/cmbn/4765

  21. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger K.: Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. Honolulu, HI, USA (2017). https://doi.org/10.48550/arXiv.1608.06993

  22. Barber, F.B.N., Oueslati, A.E.: Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model. J. Genet. Eng. Biotechnol. 22(1), 100359 (2024). https://doi.org/10.1016/j.jgeb.2024.100359

    Article  Google Scholar 

  23. Wang, H., Xu, S., Fang, K.B., Dai, Z.S., Wei, G.Z., Chen, L.F.: Contrast-enhanced magnetic resonance image segmentation based on improved U-Net and Inception-ResNet in the diagnosis of spinal metastases. J. Bone Oncol. 42, 100498 (2023). https://doi.org/10.1016/j.jbo.2023.100498

    Article  Google Scholar 

  24. Khan, M.N., Das, S., Liu, J.: Predicting pedestrian-involved crash severity using inception-v3 deep learning model. Accid. Anal. Prev. 197, 107457 (2024). https://doi.org/10.1016/j.aap.2024.107457

    Article  Google Scholar 

  25. Tang, X., Sheykhahmad, F.R.: Boosted dipper throated optimization algorithm-based Xception neural network for skin cancer diagnosis: an optimal approach. Heliyon 10, e26415 (2024). https://doi.org/10.1016/j.heliyon.2024.e26415

    Article  Google Scholar 

  26. Garg, D., Verma, G.K., Singh, A.K.: EEG-based emotion recognition using MobileNet recurrent neural network with time-frequency features. Appl. Soft Comput. 154, 111338 (2024). https://doi.org/10.1016/j.asoc.2024.111338

    Article  Google Scholar 

  27. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.: MobileNetV2: inverted residuals and linear bottlenecks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510–4520. Salt Lake City, UT, USA (2018). https://doi.org/10.48550/arXiv.1801.04381

  28. Geng, L., Hu, Y., Xiao, Z., Xi, J.: Fertility detection of hatching eggs based on a convolutional neural network. Appl. Sci. 9, 1408 (2019). https://doi.org/10.3390/app9071408

    Article  Google Scholar 

  29. Neskorodieva, T., Fedorov, E.: Method for automatic analysis of compliance of settlements with suppliers and settlements with customers by neural network model of forecast. In: Shkarlet, S., Morozov, A., Palagin, A. (eds.) Mathematical Modeling and Simulation of Systems (MODS’2020): Selected Papers of 15th International Scientific-practical Conference, MODS, 2020 June 29 – July 01, Chernihiv, Ukraine, pp. 156–165. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-58124-4_15

    Chapter  Google Scholar 

  30. Neskorodieva, T., Fedorov, E., Chychuzhko, M., Chychuzhko, V.: Metaheuristic method for searching quasi-optimal route based on the ant algorithm and annealing simulation. Radioelectron. Comput. Syst. 1, 92–102 (2022). https://doi.org/10.32620/reks.2022.1.07

    Article  Google Scholar 

  31. Images dataset. https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/

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

    Article  MathSciNet  Google Scholar 

  33. Smirnov, O., Fedorov, E., Neskorodieva, A., Neskorodieva, T.: Intellectual classification method of gymnastic elements based on combinations of descriptive and generative approach. In: CEUR Workshop Proceedings, vol. 3664, pp. 11−23 (2024). https://ceur-ws.org/Vol-3664/paper2.pdf. Accessed 21 November 2016

  34. Rwigema, J., Mfitumukiza, J., Kim, T.-Y.: A hybrid approach of neural networks for age and gender classification through decision fusion. Biomed. Signal Process. Control 66, 102459 (2021). https://doi.org/10.1016/j.bspc.2021.102459

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Cherkasy State Technological University, Shevchenko Blvd., 460, Cherkasy, 18006, Ukraine

    Anait Karapetyan, Eugene Fedorov, Irina Miroshkina, Olena Palahina & Alla Nesterenko

Authors
  1. Anait Karapetyan
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  2. Eugene Fedorov
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  3. Irina Miroshkina
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  4. Olena Palahina
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  5. Alla Nesterenko
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Corresponding author

Correspondence to Irina Miroshkina .

Editor information

Editors and Affiliations

  1. Department of Information Security and Computer Engineering, Cherkasy State Technological University, Cherkasy, Ukraine

    Emil Faure

  2. Department of Computer Science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Yurii Tryus

  3. Department of Computing Sciences, University of Vaasa, Vaasa, Finland

    Tero Vartiainen

  4. Department of Computer Science and Systems Analysis, Cherkasy State Technological University, Cherkasy, Ukraine

    Olena Danchenko

  5. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Maksym Bondarenko

  6. Department of Instrumentation, Mechatronics and Computerized Technologies, Cherkasy State Technological University, Cherkasy, Ukraine

    Constantine Bazilo

  7. Department of Software of Automated System, Cherkasy State Technological University, Cherkasy, Ukraine

    Grygoriy Zaspa

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Karapetyan, A., Fedorov, E., Miroshkina, I., Palahina, O., Nesterenko, A. (2024). Human Age Recognition Method Based on Facial Images Using an Ensemble of Neural Network Classifiers. In: Faure, E., et al. Information Technology for Education, Science, and Technics. ITEST 2024. Lecture Notes on Data Engineering and Communications Technologies, vol 222. Springer, Cham. https://doi.org/10.1007/978-3-031-71804-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-71804-5_10

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-71804-5

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