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Real-time masked face recognition using deep learning-based double generator network

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Abstract

The COVID-19 outbreak has spread rapidly worldwide since 2019. This pandemic has complicated and intricate human existence, and thousands have died from it. Due to the spread of coronavirus, people wear masks while going outside. Consequently, the system cannot identify their faces while wearing the masks. This issue can be overcome by introducing a system that recognizes masked faces of random people trained with 100 images taken from the Internet. This paper presents a novel deep learning-based double generator network to precisely identify the face behind the mask images. Initially, the gathered images are split into low- and high-frequency components using 2D-stationary wavelet transform (2D-SWT). Afterward, the Haar cascade classifier was implemented to capture the masked image biometrics to recognize the individual faces. The proposed double generator network involves two modules: edge generation and image reconstruction. The first modules consist of dilated convolutional for retrieving the relevant features from the masked face images created on the generated edges. The generated edges are reconstructed using the reflection of generated edges in the second module. Finally, the output images are reconstructed to identify the masked face. From the simulation results, the proposed framework showed effective performance based on the network parameters. The proposed network attains an accuracy of 97.08% for masked face recognition which demonstrates it achieves higher accuracy than the prior frameworks.

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References

  1. Zhang, Y., Wang, X., Shakeel, M.S., Wan, H., Kang, W.: Learning upper patch attention using dual-branch training strategy for masked face recognition. Pattern Recognit. 126, 108522 (2022). https://doi.org/10.1016/j.patcog.2022.108522

    Article  Google Scholar 

  2. Jeevan, G., Zacharias, G.C., Nair, M.S., Rajan, J.: An empirical study of the impact of masks on face recognition. Pattern Recognit. 122, 108308 (2022). https://doi.org/10.1016/j.patcog.2021.108308

    Article  Google Scholar 

  3. Ullah, N., Javed, A., Ghazanfar, M.A., Alsufyani, A., Bourouis, S.: A novel DeepMaskNet model for face mask detection and masked facial recognition. J. King Saud Univ. Comput. Inf. Sci. (2022). https://doi.org/10.1016/j.jksuci.2021.12.017

    Article  Google Scholar 

  4. Wang, J., Li, S., Luo, F.: Cross-age face recognition using deep learning model based on dual attention mechanism. In Communications, Signal Processing, and Systems: Proceedings of the 9th International Conference on Communications, J. Signal Process. Syst. pp. 1911–1919. Singapore: Springer Singapore. (2021). https://doi.org/10.1007/978-981-15-8411-4_251

  5. Cui, X., Zou, C., Wang, Z.: Remote sensing image recognition based on dual-channel deep learning network. Multimedia Tools Appl. 80(18), 27683–27699 (2021). https://doi.org/10.1007/s11042-021-11079-5

    Article  Google Scholar 

  6. Hari Shanker, EM.: IoT and AI Based Recognition and Classification of Covid 19 Persons in Public Place. Turkish Online Journal of Qualitative Inquiry, 12(8) (2021).

  7. Taneja, S., Nayyar, A., Nagrath, P.: Face mask detection using deep learning during covid-19. In Proceedings of Second International Conference on Computing, Communi, Cyber-Secur, 39–51. Springer, Singapore. (2021). https://doi.org/10.1007/978-981-16-0733-2_3

  8. Das, A., Ansari, M.W., Basak, R.: Covid-19 face mask detection using TensorFlow, Keras and OpenCV. In 2020 IEEE 17th India Council International Conference (INDICON) IEEE. 1–5 (2020). DOI: https://doi.org/10.1109/INDICON49873.2020.9342585

  9. Asif, S., Wenhui, Y., Tao, Y., Jinhai, S., Amjad, K.: Real time face mask detection system using transfer learning with machine learning method in the era of COVID-19 pandemic. In 2021 4th International Conference on Artificial Intelligence and Big Data (ICAIBD) IEEE. 70–75 (2021). DOI: https://doi.org/10.1109/ICAIBD51990.2021.9459008

  10. Vengatesan, K., Kumar, A., Karuppuchamy, V., Shaktivel, R., Singhal, A.: Face recognition of identical twins based on support vector machine classifier. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) IEEE. 577–580 (2019). DOI: https://doi.org/10.1109/I-SMAC47947.2019.9032548

  11. Jo, Y. and Park, J., Sc-fegan: Face editing generative adversarial network with user's sketch and color. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1745–1753) (2019)

  12. Priadana, A., Habibi, M.: Face detection using haar cascades to filter selfie face image on instagram. In 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT) IEEE. 6–9 (2019). DOI: https://doi.org/10.1109/ICAIIT.2019.8834526

  13. Xu, K., Wang, X., Hu, Z., Zhang, Z.: 3D face recognition based on twin neural network combining deep map and texture. In 2019 IEEE 19th International Conference on Communication Technology (ICCT) (pp. 1665–1668) IEEE. (2019). DOI: https://doi.org/10.1109/ICCT46805.2019.8947113

  14. Mabel Rose, R.A., Vasuki J., Bhavana, N.: Yolo-Vehicle: Realtime Vehicle Licence Plate Detection And Character Recognition Using Yolov7 Network. International Journal of Data Science and Artificial Intelligence (IJDSAI), 02 (01) (2024).

  15. Krishna Bikram Shah, Visalakshi S., Ranjit Panigrahi.: Seven class solid waste management-hybrid features based deep neural network, International Journal of System Design and Computing, 01(01), 1–10 (2023).

  16. Hariri, W.: Efficient masked face recognition method during the covid-19 pandemic. Signal, Image Video Process. 16(3), 605–612 (2022). https://doi.org/10.1007/s11760-021-02050-w

    Article  Google Scholar 

  17. Mandal, B., Okeukwu, A. and Theis, Y.: Masked face recognition using resnet-50. arXiv preprint arXiv:2104.08997, (2021). https://doi.org/10.48550/arXiv.2104.08997

  18. Li, Y., Guo, K., Lu, Y., Liu, L.: Cropping and attention-based approach for masked face recognition. Appl. Intell. 51(5), 3012–3025 (2021). https://doi.org/10.1007/s10489-020-02100-9

    Article  Google Scholar 

  19. Deng, H., Feng, Z., Qian, G., Lv, X., Li, H., Li, G.: MFCosface: a masked-face recognition algorithm based on large margin cosine loss. Appl. Sci. 11(16), 7310 (2021). https://doi.org/10.3390/app11167310

    Article  Google Scholar 

  20. Wang, W., Zhao, Z., Zhang, H., Wang, Z., Su, F.: MaskOut: a data augmentation method for masked face recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision 1450–1455 (2021).

  21. Geng, M., Peng, P., Huang, Y., Tian, Y.: Masked face recognition with generative data augmentation and domain constrained ranking. In Proceedings of the 28th ACM International Conference on Multimedia, 2246–2254 (2020). https://doi.org/10.1145/3394171.3413723

  22. Ding, F., Peng, P., Huang, Y., Geng, M., Tian, Y.: Masked face recognition with latent part detection. In Proceedings of the 28th ACM international Conference on multimedia, 2281–2289, (2020). https://doi.org/10.1145/3394171.3413731

  23. Malakar, S., Chiracharit, W., Chamnongthai, K. and Charoenpong, T.: May. Masked face recognition using principal component analysis and deep learning. In 2021 18th International conference on electrical engineering/electronics, computer, telecommunications and information technology (ECTI-CON) (pp. 785–788) (2021) IEEE. DOI: https://doi.org/10.1109/ECTI-CON51831.2021.9454857

  24. Mandal, B., Okeukwu, A. and Theis, Y., 2021. Masked face recognition using resnet-50. arXiv preprint arXiv:2104.08997. https://doi.org/10.48550/arXiv.2104.08997

  25. Adhinata, F.D., Rakhmadani, D.P., Wibowo, M., Jayadi, A.: A deep learning using DenseNet201 to detect masked or non-masked face. JUITA: J. Inform. 9(1), 115–121 (2021). https://doi.org/10.30595/juita.v9i1.9624

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the reviewers for all of their careful, constructive, and insightful comments in relation to this work.

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Authors and Affiliations

  1. Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, Tamil Nadu, 603203, India

    G. Sumathy

  2. Department of Masters in Computer Application, MEASI Institute of Information Technology, Chennai, Tamilnadu, India

    M. Usha

  3. Department of Electronics & Communication Engineering, Panimalar Engineering College, Chennai, 600123, India

    S. Rajakumar

  4. Department of Computer Science and Engineering, Centre for Future Networks and Digital Twin, Sri Eshwar Engineering College, Coimbatore, India

    P. Jayapriya

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Contributions

The authors confirm contribution to the paper as follows: study conception and design: SG, UM, RS, and JP; data collection: SG, UM, RS, and JP; analysis and interpretation of results: SG, UM, RS, and JP; and draft manuscript preparation: SG, UM, RS, and JP. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to G. Sumathy.

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Sumathy, G., Usha, M., Rajakumar, S. et al. Real-time masked face recognition using deep learning-based double generator network. SIViP (2024). https://doi.org/10.1007/s11760-024-03155-8

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