Effective approach of face mask position detection and recognition
Abstract
During the 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 the face mask and position of the face mask, i.e., whether the mask is correctly worn or not. The proposed model could play an instrumental role in face recognition. In the first stage, with the help of the Viola-Jones algorithm, the model detects the position of the face mask. In the second stage, we identify the person with a modified pre-trained face mask recognition that the that the 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 and greatest performance.
References
[1]Jayaweera M, Perera H, Gunawardana B, et al. Transmission of COVID-19 virus by droplets and aerosols: A critical review on the unresolved dichotomy. Environmental Research. 2020; 188: 109819. doi: 10.1016/j.envres.2020.109819
[2]Eikenberry SE, Mancuso M, Iboi E, et al. To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infectious Disease Modelling. 2020; 5: 293-308. doi: 10.1016/j.idm.2020.04.001
[3]Ejaz MdS, Islam MdR. Masked Face Recognition Using Convolutional Neural Network. In: Proceedings of the 2019 International Conference on Sustainable Technologies for Industry 40 (STI); 24-25 December 2019; Dhaka, Bangladesh. doi: 10.1109/sti47673.2019.9068044
[4]Du H, Shi H, Liu Y, et al. Towards NIR-VIS Masked Face Recognition. IEEE Signal Processing Letters. 2021; 28: 768-772. doi: 10.1109/lsp.2021.3071663
[5]Ding F, Peng P, Huang Y, et al. Masked Face Recognition with Latent Part Detection. In: Proceedings of the 28th ACM International Conference on Multimedia; 12-16 October 2020; Seattle, WA, USA. doi: 10.1145/3394171.3413731
[6]Damer N, Boutros F, Süßmilch M, et al. Extended evaluation of the effect of real and simulated masks on face recognition performance. IET Biometrics. 2021; 10(5): 548-561. doi: 10.1049/bme2.12044
[7]Deng J, Guo J, An X, et al. Masked Face Recognition Challenge: The InsightFace Track Report. In: Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW); 11-17 October 2021; Montreal, BC, Canada. pp. 1437-1444. doi: 10.1109/iccvw54120.2021.00165
[8]Rahman MH, Jannat MKA, Islam MS, et al. Real-time face mask position recognition system based on MobileNet model. Smart Health. 2023; 28: 100382. doi: 10.1016/j.smhl.2023.100382
[9]Venkateswarlu IB, Kakarla J, Prakash S. Face mask detection using MobileNet and Global Pooling Block. 2020 IEEE 4th Conference on Information & Communication Technology (CICT); 3-5 December 2020; Chennai, India. pp. 1-5. doi: 10.1109/cict51604.2020.9312083
[10]Ullah N, Javed A, Ali Ghazanfar M, et al. A novel DeepMaskNet model for face mask detection and masked facial recognition. Journal of King Saud University - Computer and Information Sciences. 2022; 34(10): 9905-9914. doi: 10.1016/j.jksuci.2021.12.017
[11]Fan X, Jiang M. RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic. In: Proceedings of the 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC); 17-20 October 2021; Melbourne, Australia. pp. 832-837. doi: 10.1109/smc52423.2021.9659271
[12]Vijitkunsawat W, Chantngarm P. Study of the Performance of Machine Learning Algorithms for Face Mask Detection. In: Proceedings of the 2020-5th International Conference on Information Technology (InCIT); 21-22 October 2020; Chonburi, Thailand. pp. 39-43. doi: 10.1109/incit50588.2020.9310963
[13]Kumar RS, Rajendran A, Amrutha V, et al. Deep Learning Model for Face Mask Based Attendance System in the Era of the Covid-19 Pandemic. In: Proceedings of the 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS); 19-20 March 2021; Coimbatore, India. pp. 1741-1746. doi: 10.1109/icaccs51430.2021.9441735
[14]Mundial IQ, Ul Hassan MS, Tiwana MI, et al. Towards Facial Recognition Problem in COVID-19 Pandemic. In: Proceedings of the 2020 4rd International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM); 3-4 September 2020; Medan, Indonesia. pp. 210-214. doi: 10.1109/elticom50775.2020.9230504
[15]Qin B, Li D. Identifying Facemask-Wearing Condition Using Image Super-Resolution with Classification Network to Prevent COVID-19. Sensors. 2020; 20(18): 5236. doi: 10.3390/s20185236
[16]Zhang JP, Li ZW, Yang J. A parallel SVM training algorithm on large-scale classification problems. In: Proceedings of the 2005 International Conference on Machine Learning and Cybernetics; 18-21 August 2005, Guangzhou, China. Volume 3. pp. 1637-1641. doi: 10.1109/icmlc.2005.1527207
[17]Chowdary GJ, Punn NS, Sonbhadra SK, Agarwal S. Face Mask Detection using Transfer Learning of InceptionV3. Available online: https://www.arxiv.org/pdf/2009.08369 (accessed on 10 May 2023).
[18]Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR 2001; 8-14 December 2001; Kauai, HI, USA. doi: 10.1109/cvpr.2001.990517
[19]Viola P, Jones MJ. Robust Real-Time Face Detection. International Journal of Computer Vision. 2004; 57(2): 137-154. doi: 10.1023/B:VISI.0000013087.49260.fb
[20]Vikram K, Padmavathi S. Facial parts detection using Viola Jones algorithm. In: Proceedings of the 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS); 6-7 January 2017; Coimbatore, India. pp. 1-4. doi: 10.1109/icaccs.2017.8014636
[21]Geng M, Peng P, Huang Y, et al. Masked Face Recognition with Generative Data Augmentation and Domain Constrained Ranking. In: Proceedings of the 28th ACM International Conference on Multimedia; 12-16 October 2020; Seattle, WA, USA. pp. 2246-2254. doi: 10.1145/3394171.3413723
[22]Liu R, Ren Z. Application of Yolo on Mask Detection Task. 2021 IEEE 13th International Conference on Computer Research and Development (ICCRD); 5-7 January 2021; Beijing, China. pp. 130-136. doi: 10.1109/iccrd51685.2021.9386366
Copyright (c) 2023 Om Pradyumana Gupta, Arun Prakash Agarwal, Om Pal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it for any purpose, even commercially, under the condition that the authors are given credit. With this license, authors hold the copyright.
