Effective approach of face mask position detection and recognition

  • Om Pradyumana Gupta Department of Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh 201 306, India
  • Arun Prakash Agarwal Department of Computer Science & Application, Sharda University, Greater Noida, Uttar Pradesh 201 306, India
  • Om Pal Department of Computer Science, University of Delhi, New Delhi 110 007, India
DOI: https://doi.org/10.59400/issc.v3i1.467
Keywords: DeepMaskNet; Viola-Jones; Convolutional neural network; Pooling layers; Softmax

Abstract

During recent COVID-19 pandemic across the world, face masks became necessary to stop the spread of infection. This has led to challenges with effective detection and recognition of human faces using the existing face detection systems. This paper proposes a Convolutional Neural Network (CNN) based face mask recognition system, which offers two solutions—recognition of the person wearing face mask and position of face mask i.e., whether the mask is correctly worn or not. The proposed model could play instrumental role of face recognition. In the first stage, with the help of Viola-Jones algorithm, the model detects the position of the face mask. In the second stage, we identify the person with by a modified pre-trained face mask recognition DeepMaskNet model facilitates in identifying the person. The proposed model achieves an accuracy of 94% in detecting the face mask position and 99.96% in identifying the masked person. Lastly, a comparison with the existing models is detailed, proving that the proposed model achieves the highest greater performance.

Author Biographies

Om Pradyumana Gupta, Department of Computer Science & Engineering, Sharda University, Greater Noida, Uttar Pradesh 201 306, India

Om Pradyumana Gupta is pursuing his Ph.D. in the field of Artificial Intelligence, Computer Vision and Deep Learning from Sharda University, Greater Noida, India. Earlier he has received degree of M.Tech. in Information Technology and MCA. Presently he is working as Scientist-E in National Informatics Centre, Ministry of Electronics and Information Technology, Govt. of India. He is involved in a number of projects of national level. Gov.in Secure Intranet, Dididhan Dashboard, Electronics Transaction Aggregation and Analysis Layer (eTaal) are among few of them. His area of interest includes Artificial Intelligence, Computer Vision and Deep Learning.

Arun Prakash Agarwal, Department of Computer Science & Application, Sharda University, Greater Noida, Uttar Pradesh 201 306, India

Dr. Arun Prakash Agrawal is currently working as a Professor with the Department of Computer Science & Application and Engineering at Sharda University, Greater Noida, India. He did PhD and Masters in Computer Science and Engineering from Guru Gobind Singh Indraprastha University, New Delhi. He was the gold medalist of his Batch. Prior to his current assignment he has served many academic institutions including Amity University, Noida. He has also taught short term courses at Swinburne University of Technology, Melbourne, Australia and Amity Global Business School, Singapore. His research interests include Machine Learning, AI and Soft Computing.

Om Pal, Department of Computer Science, University of Delhi, New Delhi 110 007, India

Dr. Om Pal received B.E. in Computer Science & Engineering from Dr. B. R. Ambedkar University, Agra, MBA(O&M) from IGNOU, MS (Research) in field of Cryptography from IIT Bombay and Ph.D. in field of Cyber Security from Jamia Millia Islamia, New Delhi. Presently he is working as an Associate Professor in Department of Computer Science, University of Delhi (Central University), North Campus, Delhi. His area of interest includes Cryptography, Information Security, Cyber Security, Network Security, Post-Quantum Cryptography, Quantum Computing, Blockchain Technology, Algorithms Analysis and Design, Cyber Law, Artificial Intelligence for Cyber Security, Machine learning for Cyber Security, Theory of Automata, Operation Research and Mathematics.

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Published
2024-03-29
How to Cite
Gupta, O. P., Agarwal, A. P., & Pal, O. (2024). Effective approach of face mask position detection and recognition. Information System and Smart City, 3(1), 467. https://doi.org/10.59400/issc.v3i1.467
Issue
Vol. 3 No. 1 (2023)
Section
Original Research Articles

Copyright (c) 2024 Om Pradyumana Gupta, Arun Prakash Agarwal, Om Pal

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