Skip to main content
Log in

Deep transfer learning based real time face mask detection system with computer vision

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The COVID -19 pandemic has great impact in almost all life in the world. Till a immunogen is identified, everyone must be very careful about the spread of corona virus. Wearing a mask will definitely prevent us from corona virus. This results from the requirement to keep everyone safe from the virus's propagation. However, because current systems can't more effectively match faces with masks, the steps made to stop the virus' propagation present a problem for security and surveillance systems. Three methods are proposed to identify or detect whether person is sporting a mask or improper wearing of mask or a person with no mask. The ResNet152V2,VGG16 will work with still pictures and jointly works with a live video stream. A mask detection dataset consists of pictures of both mask wearing and without mask wearing. OpenCV is used to do the real time face detection. It checks whether mask is properly worn over the nose area by completely covering chin and mouth. The proposed mask symbol is least complicated in structure and provides fast results so it is used in video surveillance. Mass screening is possible and thus utilized in thronged places like railway stations, bus stops, markets, streets, mall entrances, schools, colleges and so on. In proposed system, ResNet152V2 model is used for robust face detection. The head based model is selected for classifying faces as masked or non-masked. Finally computer vision concepts are added to improve performance on video streams. The results demonstrate that the proposed model improves accuracy by 99.1%, performance of precision by 99.2%, F1-score by 99.1%. It is identified that the proposed model ResNet152V2 accomplished highest results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Data availability

No.

References

  1. Abboah-Offei M, Salifu Y, Adewale B, Bayuo J, Ofosu-Poku R, Opare-Lokko EBA (2021) A rapid review of the use of face mask in preventing the spread of COVID-19. Nat Lib Med. https://doi.org/10.1016/j.ijnsa.2020.100013

  2. Abdulkareem KH, Mutlag AA, Dinar AM, Frnda J, Mohammed MA, Zayr FH, Lakhan A, Kadry S, Khattak HA, Nedoma J 2022 Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments. Comput Intell Neurosci. 1–17. https://doi.org/10.1155/2022/5012962

  3. Bunge E, Simelane S, GFashotoAkinnuwesi SB (2021) Application of deep learning and machine learning models to detect COVID-19 face masks - A review. Sustain Oper Comput. 2:235–245. https://doi.org/10.1016/j.susoc.2021.08.001

    Article  Google Scholar 

  4. Chavali C, Agrawal H, Mahendru A, Batra D (2014) Object-proposal evaluation protocol is 'Gameable'. arXiv:1505.05836

  5. Chollet F (2017) Xception: deep learning with depthwise separable convolutions. arXiv:1610.02357

  6. ChowdaryGJ, Punn N (2020) Face mask detection using transfer learning of inceptionV3. Big Data Anal, 81–90. https://doi.org/10.1007/978-3-030-66665-1_6

  7. Das A, Ansari MW, Basak R (2020) Covid-19 face mask detection using tensorflow, keras and openCV. 2020 IEEE 17th Ind Council Int Conf (INDICON). https://doi.org/10.1109/INDICON49873.2020.9342585.

  8. Dong J, Yan QC, Yuille A (2014) Towards Unified Object Detection and Semantic Segmentation. Euro Conf Comput Vis, pp. 299–314

  9. Eikenberry SE, Mancuso M, Iboi E, Phan T, Eikenberry K, Kuang Y, Kostelich E, Gume AB (2020) To mask or not to mask: Modeling the potential for face mask use by the general public to curtail the COVID-19 pandemic. Infect Dis Model 5:293–308. https://doi.org/10.1016/j.idm.2020.04.001

    Article  Google Scholar 

  10. Geetha R, Balasubramanian M, Devi KR (2022) COVID detection: deep convolutional neural networks-based automatic detection of COVID-19 with chest x-ray images. Res Biomed Eng. https://doi.org/10.1007/s42600-022-00230-2

    Article  Google Scholar 

  11. Geetha R, Ramya DK, Balasubramanian M (2021) Prediction of domestic power peak demand and consumption using supervised machine learning with smart meter dataset. Multimed Tools Appl 80(13):1–19. https://doi.org/10.1007/s11042-021-10696-4

    Article  Google Scholar 

  12. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. IEEE Conf Comput Vis Patt Recognit. https://doi.org/10.1109/CVPR.2014.81

    Article  Google Scholar 

  13. Grassi M, Faundez-Zanuy M (2007) Face recognition with facial mask. Application and Neural Networks in Springer book series. https://doi.org/10.1007/978-3-540-73007-1_85

  14. He K, Zhang X, Ren S, Sun J (2016) Identity mappings in deep residual networks. Euro Conf Comput Vis ECCV, pp. 630–645

  15. Hussain GKJ, Priya R, Rajarajeswari S, Prasanth P, Niyazuddeen N (1916) The face mask detection technology for image analysis in the covid-19 surveillance system. J Phys Conf Series 1:012084. https://doi.org/10.1088/1742-6596/1916/1/012084

    Article  Google Scholar 

  16. Karnati Mohan, Seal Ayan, Yazidi Anis, Krejcar Ondrej (2021) LieNet: A deep convolution neural networks framework for detecting deception. IEEE Explore. https://doi.org/10.1109/TCDS.2021.3086011

    Article  Google Scholar 

  17. Kisan S, Nayak S, Chawda A, Mishra S, Mohanty SN (2020) Face Shape classification based on modified relative improved differential box count meth-od. Int J Adv Sci Technol 29(3):3878–3889

    Google Scholar 

  18. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: Single shot multibox detector. Euro Conf Comput Vis ECCV 2016: pp. 21–37

  19. Mangla M, Sayyad A, Mohanty SN (2021) An AI and Computer vision-based face mask recognition and detection system. IEEE Explore. https://doi.org/10.1109/ICSCCC51823.2021.9478175

    Article  Google Scholar 

  20. Matthias D, Managwu C (2021) Face mask detection paper in covid-19 project. https://doi.org/10.13140/RG.2.2.18493.59368

  21. Mohammed HJ, Al-Fahdawi S, Al-Waisy AS, Zebari DA, Ibrahim DA, Mohammed MA, Kadry S, Kim J (2022) ReID-DeePNet: A hybrid deep learning system for person re-identification. Mathematics. 10(19):3530. https://doi.org/10.3390/math10193530

    Article  Google Scholar 

  22. Mohammed MA, Al-Khateeb B, Salama MY, Karrar AMSK, Abdulkareem H, Garcia-Zapirain B (2022) Novel crow swarm optimization algorithm and selection approach for optimal deep learning COVID-19 Diagnostic Model. Comput Intell Neurosci. https://doi.org/10.1155/2022/1307944

    Article  Google Scholar 

  23. Mohan K, Seal A, Krejcar O, Yazidi A (2020) Facial expression recognition using local gravitational force descriptor-based deep convolution Neural Networks. IEEE Explore 70:1–12. https://doi.org/10.1109/TIM.2020.3031835

    Article  Google Scholar 

  24. Mohan K, Seal A, Krejcar O, Yazidi A (2021) FER-net: Facial expression recognition using deep neural net. Neur Comput Appl 33:9125–9136. https://doi.org/10.1007/s00521-020-05676-y

    Article  Google Scholar 

  25. Mohan K, Seal A, Sahu G, Yazidi A, Krejcar O (2022) A novel multi-scale based deep convolutional neural network for detecting COVID-19 from X-rays. Appl Soft Comput 125:109109. https://doi.org/10.1016/j.asoc.2022.109109

    Article  Google Scholar 

  26. Mukhlif AA, Al-Khateeb B, Mohammed MA (2023) Classification of breast cancer images using new transfer learning techniques. Iraqi J Comput Sci Math, 4, 167-180. https://doi.org/10.52866/ijcsm.2023.01.01.0014

  27. Mukhlif AA, Al-Khateeb B, Mohammed MA (2023) Incorporating a novel dual transfer learning approach for medical images. Sensors 23(2):570. https://doi.org/10.3390/s23020570

    Article  Google Scholar 

  28. Rahman MM, Manik MMH, Islam MM, Mahmud S, KimJ-H (2020) An automated system to limit COVID-19 using facial mask detection in smart city network. 2020 IEEE International IoT Electronics and Mechatronics Conference (IEMTRONICS).https://doi.org/10.1109/IEMTRONICS51293.2020.9216386

  29. Razavi M, Alikhani H, Janfaza V, Sadeghi B, Alikhani E (2022) An automatic system to monitor the physical distance and face mask wearing of construction workers in COVID-19 pandemic. SN Comput Sci, 3 Article number: 27

  30. Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: Towards real-time object detection with region proposal networks. arXiv:1506.01497

  31. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) MobileNetV2: Inverted residuals and linear bottlenecks. 2018 IEEE/CVF Conf Comput Vis Patt Recognit (CVPR).Vol:1PP4510–4520. https://doi.org/10.1109/CVPR.2018.00474

  32. Sanjaya SA, Rakhmawan SA (2020) Face Mask detection using MobileNetV2 in the era of COVID-19 Pandemic. Int Conf Data Anal Business Indust Way Towards Sustain Econ (ICDABI). https://doi.org/10.1109/ICDABI51230.2020.9325631

    Article  Google Scholar 

  33. Sethi S, Kathuria M, Kaushik T (2021) Face mask detection using deep learning: An approach to reduce risk of Corona virus spread. PMCID. https://doi.org/10.1016/j.jbi.2021.103848

    Article  Google Scholar 

  34. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv.org

  35. Singh S, Ahuja U, Kumar M, Kumar K, Sachdeva M (2021) Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment. Multimed Tools Appl 80:19753–19768

    Article  Google Scholar 

  36. Teboulbi S, Messaoud S, Hajjaji MA, Mtibaa A (2021) Real-time implementation of AI-based face mask detection and social distancing measuring system for COVID-19 prevention. Sci Program 2021:21. https://doi.org/10.1155/2021/8340779

    Article  Google Scholar 

  37. Tomás J, Rego A, Lloret J (2021) Incorrect facemask-wearing detection using convolutional neural networks with transfer learning. Health care 9(8):1050. https://doi.org/10.3390/healthcare9081050

    Article  Google Scholar 

  38. Viola P, Jones M (2001) Robust real-time face detection. In IEEE International Conference on Computer Vision. ICCV 2001.ISBN:0–7695–1143–0. DOI: https://doi.org/10.1109/ICCV.2001.937709.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Geetha.

Ethics declarations

Conflicts of interest

No Conflict of interest.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Balasubramanian, M., Ramyadevi, K. & Geetha, R. Deep transfer learning based real time face mask detection system with computer vision. Multimed Tools Appl 83, 17511–17530 (2024). https://doi.org/10.1007/s11042-023-16192-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-16192-1

Keywords