CFP last date
20 May 2024
Reseach Article

Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model

by Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 2
Year of Publication: 2024
Authors: Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam

Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam . Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model. International Journal of Computer Applications. 186, 2 ( Jan 2024), 42-51. DOI=10.5120/ijca2024923351

@article{ 10.5120/ijca2024923351,
author = { Md. Omar Faruque, Md. Rashedul Islam, Md. Touhid Islam },
title = { Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2024 },
volume = { 186 },
number = { 2 },
month = { Jan },
year = { 2024 },
issn = { 0975-8887 },
pages = { 42-51 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2024923351 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T01:29:33.031319+05:30
%A Md. Omar Faruque
%A Md. Rashedul Islam
%A Md. Touhid Islam
%T Advanced Masked Face Recognition using Robust and Light Weight Deep Learning Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 2
%P 42-51
%D 2024
%I Foundation of Computer Science (FCS), NY, USA

For public health and safety reasons, face masks were required worldwide during the COVID-19 epidemic. However, this poses challenges for face recognition systems as the face is partially covered. Face recognition is a widely used and cost-effective biometric security system, but it faces difficulties in accurately identifying individuals wearing masks. Existing algorithms for face recognition have struggled to maintain efficiency, accuracy, and performance in the context of masked faces. To address these challenges and improve cost-effectiveness, a new machine learning model is required. This manuscript describes a lightweight deep learning methodology that is flexible and efficient in recognizing masked faces. The HSTU Masked Face Dataset (HMFD) is utilized, comprising frontal and lateral faces with various colored masks. Our proposed method involves a lightweight CNN model designed to enhance the accuracy of masked face identification. To enhance operational efficiency, methods like batch normalization, dropout, and depth-wise normalization are integrated which are tailored to meet particular specifications, aiming to optimize overall performance. These techniques improve the efficiency and accuracy of the model while minimizing overall complexity. In this research, the accuracy of the model is evaluated in comparison to other well-established deep learning models, including VGG16, VGG19, Extended VGG19, MobileNet, and MobileNetV2. The results demonstrate that our lightweight deep learning model outperforms these models, achieving a high recognition accuracy of 97%. By considering the needs of the task and carefully optimizing the model architecture, our proposed method offers an effective and efficient solution for recognizing masked faces in real-world scenarios.

  1. Adjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, Present, and Future of Face Recognition: A Review. Electronics, 9(8), 1188.
  2. Ko, B. (2018). A Brief Review of Facial Emotion Recognition Based on Visual Information. Sensors, 18(2), 401.
  3. Chakraborty, B. K., Sarma, D., Bhuyan, M. K., & MacDorman, K. F. (2018). Review of constraints on vision‐based gesture recognition for human–computer interaction. IET Computer Vision, 12(1), 3-15.
  4. Egger, M., Ley, M., & Hanke, S. (2019). Emotion recognition from physiological signal analysis: A review. Electronic Notes in Theoretical Computer Science, 343, 35-55.
  5. Voulodimos, A., Doulamis, N., Doulamis, A., & Protopapadakis, E. (2018). Deep learning for computer vision: A brief review. Computational intelligence and neuroscience, 2018.
  6. Dang, K., & Sharma, S. (2017, January). Review and comparison of face detection algorithms. In 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence (pp. 629-633). IEEE.
  7. Qiu, S., Liu, Q., Zhou, S., & Wu, C. (2019). Review of artificial intelligence adversarial attack and defense technologies. Applied Sciences, 9(5), 909.
  8. Galterio, M. G., Shavit, S. A., & Hayajneh, T. (2018). A review of facial biometrics security for smart devices. Computers, 7(3), 37.
  9. Cook, C. M., Howard, J. J., Sirotin, Y. B., Tipton, J. L., & Vemury, A. R. (2019). Demographic effects in facial recognition and their dependence on image acquisition: An evaluation of eleven commercial systems. IEEE Transactions on Biometrics, Behavior, and Identity Science, 1(1), 32-41.
  10. Jeon, B., Jeong, B., Jee, S., Huang, Y., Kim, Y., Park, G. H., ... & Choi, T. H. (2019). A facial recognition mobile app for patient safety and biometric identification: Design, development, and validation. JMIR mHealth and uHealth, 7(4), e11472.
  11. Dargan, S., & Kumar, M. (2020). A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities. Expert Systems with Applications, 143, 113114.
  12. Gonzalez-Sosa, E., Fierrez, J., Vera-Rodriguez, R., & Alonso-Fernandez, F. (2018). Facial soft biometrics for recognition in the wild: Recent works, annotation, and COTS evaluation. IEEE Transactions on Information Forensics and Security, 13(8), 2001-2014.
  13. Karthik, K., Babu, R. P. A., Dhama, K., Chitra, M. A., Kalaiselvi, G., Senthilkumar, T. M. A., & Raj, G. D. (2020). Biosafety concerns during the collection, transportation, and processing of COVID-19 samples for diagnosis. Archives of Medical Research, 51(7), 623-630.
  14. Ortiz, M. R., Grijalva, M. J., Turell, M. J., Waters, W. F., Montalvo, A. C., Mathias, D., ... & Leon, R. (2020). Biosafety at home: How to translate biomedical laboratory safety precautions for everyday use in the context of COVID-19. The American journal of tropical medicine and hygiene, 103(2), 838.
  15. Souza, T. M. L., & Morel, C. M. (2021). The COVID-19 pandemics and the relevance of biosafety facilities for metagenomics surveillance, structured disease prevention and control. Biosafety and Health, 3(01), 1-3.
  16. Mills, M., Rahal, C., & Akimova, E. (2020). Face masks and coverings for the general public: Behavioural knowledge, effectiveness of cloth coverings and public messaging. The Royal Society, 26.
  17. Liu, X., & Zhang, S. (2020). COVID‐19: Face masks and human‐to‐human transmission. Influenza and other respiratory viruses, 14(4), 472.
  18. Ngan, M., Grother, P., & Hanaoka, K. (2020). Face recognition accuracy with masks using pre-COVID-19 algorithms. In NISTIR 8311.
  19. Zeng, J., Qiu, X., & Shi, S. (2021). Image processing effects on the deep face recognition system. Math. Biosci. Eng, 18(2), 1187-1200.
  20. Wang, Z., Huang, B., Wang, G., Yi, P., & Jiang, K. (2023). Masked face recognition dataset and application. IEEE Transactions on Biometrics, Behavior, and Identity Science.
  21. Wang, Z., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., ... & Liang, J. (2020). Masked Face Recognition Dataset and Application (preprint).
  22. Fan, X., & Jiang, M. (2021, October). RetinaFaceMask: A single stage face mask detector for assisting control of the COVID-19 pandemic. In 2021 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 832-837). IEEE.
  23. Shastri, B. J., & Levine, M. D. (2007). Face recognition using localized features based on non-negative sparse coding. Machine Vision and Applications, 18, 107-122.
  24. Kasar, M. M., Bhattacharyya, D., & Kim, T. H. (2016). Face recognition using neural network: a review. International Journal of Security and Its Applications, 10(3), 81-100.
  25. Fujita, M., Yoshida, T., & Hangai, S. (2006, May). A study on the effect of ROI masks on face recognition system using digital recorder. In 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings (Vol. 2, pp. II-II). IEEE.
  26. Saudagare, P. V., & Chaudhari, D. S. (2012). Facial expression recognition using neural network–An overview. International Journal of Soft Computing and Engineering (IJSCE), 2(1), 224-227.
  27. Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016). Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14 (pp. 87-102). Springer International Publishing.
  28. Wang, X., Wang, S., Wang, J., Shi, H., & Mei, T. (2019). Co-mining: Deep face recognition with noisy labels. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 9358-9367).
  29. Marjan, M. A., Hasan, M., Islam, M. Z., Uddin, M. P., & Afjal, M. I. (2022, December). Masked Face Recognition System using Extended VGG-19. In 2022 4th International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE) (pp. 1-4). IEEE.
  30. Huang, G. B., & Learned-Miller, E. (2014). Labeled faces in the wild: Updates and new reporting procedures. Dept. Comput. Sci., Univ. Massachusetts Amherst, Amherst, MA, USA, Tech. Rep, 14(003).
  31. Boulkenafet, Z., Komulainen, J., & Hadid, A. (2015, September). Face anti-spoofing based on color texture analysis. In 2015 IEEE international conference on image processing (ICIP) (pp. 2636-2640). IEEE.
  32. Neto, P. C., Boutros, F., Pinto, J. R., Damer, N., Sequeira, A. F., & Cardoso, J. S. (2021, December). Focusface: Multi-task contrastive learning for masked face recognition. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) (pp. 01-08). IEEE.
Index Terms

Computer Science
Information Sciences


Masked Face Recognition Deep Learning Convolutional Neural Network Max-pooling Lightweight CNN Covid-19 Pandemic