An Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review



After the emergence of the Coronavirus disease (Covid19) in December, 2019 the virus was confirmed by the World Health Organization (WHO) to be a dangerous virus that spread through airborne and droplets. One of the most effective way of preventing the spread of this virus is the use of facemask in public places such as banks, shopping mall, schools, offices and the likes. For this reasons people are advised to use facemask whenever they go out for their daily activities. However, some people have refused to wear the facemask in public places, thereby increasing the rate at which the virus spreads. Hence, there is a need to design an access control system for use in public places to grant access to people complying with the facemask regulations or deny access to those faulting them. Literature has shown that the use of computer vision and deep learning technology can play important role in the deployment of such access control system for face detection and facemask detection. The computer vision and deep learning techniques used in face recognition and facemask detection are describes in this systematic literature review.


Download data is not yet available.

Author Biographies

Emeka Ogbuju, Federal University Lokoja

Department of Computer Science. Lecturer.

Francisca Oladipo, Federal University Lokoja

Department of Computer Science. Head of Department.


Yan, Z. (2020). Unprecedented pandemic, unprecedented shift, and unprecedented opportunity. Hum Behav Emerg Technol.

Mackworth-Young, C., Chingono, R., Mavodza, C., McHugh, G., Tembo, M., Chik- war, C., . . . Ferrand, R. A. (2020). Community perspectives on the Covid19 response, zimbabwe. Bull World Health Organ, 85-91.

Mbunge, E., Fashoto, S. G., & Batani, J. (2021). COVID19 digital vaccination certificates and digital technologies: lessons from digital contact tracing apps. SSRN Electron.

Lewnard, J. A., & Lo, N. C. (2020). Scientific and ethical basis for social-distancing interventions against COVID19. Lancet Infect.

Amint, M., & Poonam, C. (2020). Futuristic Acess Control Method To Avoid Covid19 Transmission . International Journal of Advanced Science and Technology, 3196-3204.

Mustafa , G., & Farkhondeh, A. (2021). Deep Learning in the Detection and Diagnosis of Covid19 Using Radiology Medalities: A Systematic Review. Journal of Healthcare Engineering.

Connor, S., Taghi, M. K., & Borko, F. (2021). Deep Learning application for Covid19. Journal of Big Data.

Shilpa, S., Mamta, K., & Trilok, K. (2021). Facemask detection using deep learning: An approach to reduce risk of Coronavirus spread . Journal of Biomedical Informatics.

Elliot , M., Sakhile, S., Stephen, G. F., Boluwaji, A., & Andile, S. M. (2021). Application of deep learning and machine learning models to detect Covid19 facemasks: A review. open access.

Safa, T., Seifeddine, M., Mohamed , A. H., & Abdellatif, M. (2021). Real Time Implemenetection and Social Distancing Measuring System for Covid19 prevention. Scientific programming journal.

Samuel, A. S., & Suryo, A. R. (2020). Facemask Detection in The Era of The COVID19. Seminar Nasional official statistics (pp. 370-373). Jakarta, Indonesia: Statistics in New Normal.

Suganthalakshmi, R. M., Hafeeza, A., Abinaya, P., & Ganga, D. A. (2021). Covid19 Facemask Detection with Deep Learning and Computer Vision. International Journal of Engineering Research & Technology, 73-75.

Biparnak, R., Subhadip, N., Debojit , G., Debarghya, D., Pritam, B., & Tamodip, D. (2020). MOXA: A Deep Learning Based Unmanned Approach For Real-Time Monitoring of People Wearing Medical Masks. Indian National Academy of Engineering, 509-518.

Akanksha, S., & Arun, P. S. (2020). Automatic Motorcyclist Helment Ruke Violation Detection using Tensorflow and Keras in OpenCV. Conference on Electrical Electronics and Computer Science. Bangalore: Journal of research proceedings.

Bu, W., Xiao, J., Zhou, C., Yang, M., & Peng, C. (2017). A cascade framework for masked face detection. IEEE International Conference. Robot Autom.

Jiang, M., & Fan, X. (2020.). Retinamask: a facemask detector. ArXiv.

Qin, B., & Li, D. (2020). Identifying facemask-wearing condition using image super-resolution with classification network to prevent COVID19. Switzerland: Sensors.

Loey, M., Manogaran, G., Taha, M., & Khalifa, N. (2021). A hybrid deep trans- fer learning model with machine learning methods for facemask detection in the era of the COVID19 pandemic. Meas J. Int. Meas.

Chowdary, G., Punn, N., Sonbhadra, S., & Agarwal, S. (2020). Facemask detection using transfer learning of inceptionV3. ArXiv.

Yadav, S. (2020). Deep learning based safe social distancing and facemask detection in public areas for COVID19 safety guidelines adherence. International Journal of Research in Applied Science and Engineering Technology.

Inamdar, M., & Mehendale, N. (2020). Real-time facemask identification using facemasknet deep learning network. SSRN Electron Journal.

Rahman, M., Manik, M., Islam , M., & Mahmud, S. (2020). An auto- mated system to limit COVID19 using facial mask detection in smart city net- work. Mechatronics Conference Proceedings. EMTRONICS.

Addagarla, S., Kalyan, C. G., & Anitha, P. (2020). Real time multi-scale facial mask detection and classification using deep transfer learning techniques. International Journal of Advanced Trends in Computer Science and Engineering.

Gathani, J., & Shah, K. (2020). Detecting masked faces using region-based convolutional neural network. IEEE 15th International Conference, (pp. 156-161). India.

Nagrath, P., Jain, R., Madan, A., Arora, R., & Katari, P. (2021). A real time DNN-based facemask detection system using single shot multibox detector and MobileNetV2. Sustain.

Joshi, A., Joshi, S., Kanahasabai, & Kapil, R. (2020). Deep learning framework to detect facemasks from video footage. 12th international conference of computer intell communication network.

Subhamastan, R. T., Anjali , D. S., & Dileep, P. (2020). A novel approach to detect facemask to control covid using deep learning. European Journal of Molecular and clinical Medicine.

Bhuiyan, M., Khushbu, S., & Islam, M. (2020). A deep learning based assistive system to classify COVID19 facemask for human safety with YOLOv3. 11th International Conference of Computer Comminication. Network Technology.

Mohan, P., Paul, A., & Chirania, A. (2020). A tiny CNN architecture for medical facemask detection for resource-constrained endpoints. ArXiv.

Wang, Z., Wang, P., Louis, P., Wheless, L., & Huo, Y. (2021). WearMask: Fast In-browser Facemask Detection with Serverless Edge Computing for COVID19. International Journal of Computing.

Chavda, A., Dsouza, J., Badgujar, S., & Daman, A. (2020). Multi-stage CNN architecture for facemask detection. ArXiv.

Venkatesan , S., & Madane, S. (2010). Face detection by hybrid genetic and ant colony opti- mization algorithm. International Journal of Computer Application.

Chen, Y., Hu, M., Hua, C., Zhai, G., & Zhang , J. (2020). Facemask Assistant: Detection of Facemask Service Stage Based on Mobile Phone. ArXiv.

Roy, B., Nandy, S., Ghosh, D., Dutta, D., Biswas, P., & Das, T. (2020). MOXA: a deep learning based unmanned approach for real-time monitoring of people wearing medical masks. Conference of National Academoy of Engineering. India.

Lin, H., Tse, R., Tang, S. k., Chen, Y., Ke, W., & Pau, G. (2021). Near-Realtime Facemask Wearing Recognition Based on Deep Learning. Institution of Electronics and Electrical Engineering (IEEE), 1-7.

Taneja, S., Nayyar, A., Vividha, & Nagrath, P. (2021). Facemask Detection Us- ing Deep Learning During COVID19. Springer, 39-51.

Degadwala, S., Vyas, D., Chakraborty, U., & Dider, A. R. (2021). Yolo-v4 deep learning model for medical facemask detection. International Conference Proceedings of Artificial Inteligence Smart System (pp. 209-213). Institute of Electrical and Electronics Engineers (IEEE).

Sagayam, K. (2021). CNN-based mask detection system using openCV and MobileNetV2. 3rd International Conference of Signal Process Communication (pp. 115-119). Institute of Electrical and Electronics Engineers (IEEE).

Kodali, R., & Dhanekula, R. (2021). Facemask detection using deep learning. International Conference on Computer Communication and Informatics (ICCCI). Institute of Electrical and Electronics Engineers (IEEE).

Snyder, S., Husari, G., & Thor. (2021). A deep learning approach for facemask detec- tion to prevent the COVID19 pandemic. IEEE SOUTHEASTCON. Institute of Electrical and Electronics Engineers (IEEE).

Hussain, S., Yu, Y., Ayoub, M., Khan, A., Khan, A., Rehman, R., & Wahlid, J. A. (2021). IoT and deep learning based approach for rapid screening and facemask detection for infection spread control of COVID19. Journal of Applied Science, 3495.

Das, A., Ansari, M. W., & Basak, R. (2020). Covid-19 Face Mask Detection Using TensorFlow, Keras and OpenCV. In 2020 IEEE 17th India Council International Conference (INDICON) (pp. 1-5). India: IEEE.

Ferdousi, R., Israt, J. R., Nafisa, F., & Jia, U. (2019). AnAssistive model for visually impaired people using YOLO and MTCNN. ACM International Conference Proceeding Series, (pp. 225-230). Dhaka.

Vasiliy, L. (2019). Face DetectNet: Face detection via fully-convolutional network. Computer Optics, 238-244.

Nadir, K. B., Mikel, V.-C., Jose, R., Jose, R. A.-S., Diaz, M. A., Ferrandez, J. M., . . . Stambouli, T. B. (2019). Real-Time Emotional Recognition for Sociable Robotics Based on Deep Neural Networks Ensemble. Computer Science , 171–180.

Seunghyun, L., Minseop, K., & Inwhee, J. (2019). SGNet: Design of optimized DCNN for real-time face detection,. in Communications in Computer and Information Science,, (pp. 200-209).

Wang, Y., & Zheng, J. (2018). Real-time face detection based on YOLO. n 1st IEEE International Conference on Knowledge Innovation and Invention, (pp. 221-224).

Gretchel, K. A., Ivan, D. E., Jerome, V. M., Ofelia, B. O., Reginald, S. B., & Leonard, A. U. (2018). Head detection and tracking using OpenCV. 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (pp. 1-5). IEEE.

Gupta, S. (2018). Facial emotion recognition in real-time and static images. In 2018 2nd international conference on inventive systems and control (pp. 553-560). IEEE.

Lee, H.-W., Peng, F.-F., Lee, X.-Y., Dai, H.-N., & Zhu, Y. (2018). Research on face detection under different lighting. IEEE International Conference on Applied System Invention(ICASI) (pp. 1145-1148). IEEE.

Gupta, N., Sharma, P., Deep, V., & Shukla, V. K. (2020). Automated attendance system using OpenCV. In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 1226-1230). IEEE.

Hoque, M. A., Islam, T., Ahmed, T., Amin, A., & Amin, A. (2020). Autonomous face detection system from real-time video streaming for ensuring the intelligence security system. 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (pp. 261-265). IEEE.

Mehariya, J., Gupta, C., Pai, N., Koul, S., & Gadakh, P. (2020). Counting Students using OpenCV and Integration with Firebase for Classroom Allocation. 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) (pp. 624-629). IEEE.

Sriratana, W., Mukma, S., Tammarugwattana, N., & Sirisantisamrid, K. (2018). Application of the OpenCV-Python for Personal Identifier Statemen. 2018 International Conference on Engineering, Applied Sciences, and Technology (ICEAST) (pp. 1-4). IEEE.

Patel, R., Patel, M., & Patel, J. (2018). Real Time Somnolence Detection System In OpenCV Environment for Drivers. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 407-410). IEEE.

Michael, J. C., Matthew, J. M., & Mustaque, A. (2000). Generalized role-based access control for securing future applications. In Proceedings of the National Information Systems Security Conference.

Vijayalakshmi, A., & Soon, A. C. (2004). An authorization model for geospatial data. IEEE Transactions on Dependable and Secure Computing, 238–254.

Elisa, B., Barbara, C., Maria, L. D., & Paolo, P. (2005). GEO-RBAC: a spatially aware RBAC. In Proceedings of the 10th ACM Symposium on Access Control Models and Technologies, (pp. 29–37). New York.

Indrakshi, R., & Manachai, T. (2007). A spatio-temporal role-based access control model. In Proceedings of the 21st annual IFIP WG 11.3 working conference on Data and applications security, (pp. 211–226). Berlin.

Arjmand, S., Arif, G., & Elisa, B. (2007). A framework for specification and verification of generalized spatio-temporal role based access control model. West Lafayette: Technical Report CERIAS-TR-2007-08, Center for Education and Research in Information Assurance and Security.

Subhendu, A., Samrat, M., Shamik, S., & Arun, K. M. (2009). Role based access control with spatiotemporal context for mobile applications. Transactions on Computational Science IV: Special Issue on Security in Computing, (pp. 177–199).



How to Cite

Abiodun, T., Ogbuju, E., & Oladipo, F. (2022). An Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review. Journal of Applied Artificial Intelligence, 3(1), 34–46.