IDEAS home Printed from https://ideas.repec.org/a/zib/zbnaim/v6y2022i2p43-46.html
   My bibliography  Save this article

A Deep Learning Model For Face Recognition In Presence Of Mask

Author

Listed:
  • Kalembo Vikalwe Shakrani

    (Sharda University, Greater Noida, India)

  • Ngonidzashe Mathew Kanyangarara

    (Sharda University, Greater Noida, India)

  • Prince Tinashe Parowa

    (Sharda University, Greater Noida, India)

  • Vibhor Gupta

    (Sharda University, Greater Noida, India)

  • Rajendra Kumar

    (Sharda University, Greater Noida, India)

Abstract

Image classifications and object detection are common study topics in the rapidly expanding technological advancements to identify and detect real-time problems in major federal fields like public places, airports and army bases using webcams and surveillance cameras opensource platforms. The goal of this study is to suggest Open Source Computer Vision (OpenCV) and Convolutional Neural Network (CNN) techniques for identifying a person in presence of face mask from image datasets and real-time (live streaming video). For experimental purpose a parent directory consisting of three main directories (i.e., training, testing and validation sets) and two sub directories inside those containing Mask (M) and No Mask (N), respectively are used. Mask subdirectories have images of people wearing masks and the vice versa is for Non Mask. Total 1006 images are used including 503 Mask and 503 No-Mask. The data augmentation pre-processing method is used to increase the dataset size to improve the accuracy of the suggested model. The proposed system uses a camra inbuilt on drone to capture real-time image for recognition using Conventional Neural Network (CNN). The proposed model is constructed, compiled and trained using Tensor flow and Keras. The final training accuracy recorded is 0.93, while the validation accuracy recorded is 0.94, the training loss is 0.17, the validation loss here observed is 0.1672, and the test loss is 0.15. The classification accuracy of the proposed system observed is 0.95.

Suggested Citation

  • Kalembo Vikalwe Shakrani & Ngonidzashe Mathew Kanyangarara & Prince Tinashe Parowa & Vibhor Gupta & Rajendra Kumar, 2022. "A Deep Learning Model For Face Recognition In Presence Of Mask," Acta Informatica Malaysia (AIM), Zibeline International Publishing, vol. 6(2), pages 43-46, April.
    • Handle: RePEc:zib:zbnaim:v:6:y:2022:i:2:p:43-46
    DOI: 10.26480/aim.02.2022.43.46
    as

    Download full text from publisher

    File URL: https://actainformaticamalaysia.com/archives/AIM/2aim2022/2aim2022-43-46.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.26480/aim.02.2022.43.46?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Irfan & Nadeem Akhtar & Munir Ahmad & Farrukh Shahzad & Rajvikram Madurai Elavarasan & Haitao Wu & Chuxiao Yang, 2021. "Assessing Public Willingness to Wear Face Masks during the COVID-19 Pandemic: Fresh Insights from the Theory of Planned Behavior," IJERPH, MDPI, vol. 18(9), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Laurent-Simpson, 2023. "COVID-19 and Masking Disparities: Qualitative Analysis of Trust on the CDC’s Facebook Page," IJERPH, MDPI, vol. 20(12), pages 1-18, June.
    2. Yongrong Xin & Muhammad Irfan & Bilal Ahmad & Madad Ali & Lanqi Xia, 2023. "Identifying How E-Service Quality Affects Perceived Usefulness of Online Reviews in Post-COVID-19 Context: A Sustainable Food Consumption Behavior Paradigm," Sustainability, MDPI, vol. 15(2), pages 1-16, January.
    3. Munir Ahmad & Nadeem Akhtar & Gul Jabeen & Muhammad Irfan & Muhammad Khalid Anser & Haitao Wu & Cem Işık, 2021. "Intention-Based Critical Factors Affecting Willingness to Adopt Novel Coronavirus Prevention in Pakistan: Implications for Future Pandemics," IJERPH, MDPI, vol. 18(11), pages 1-28, June.
    4. Shanmugavel, Nagarajan & Balakrishnan, Janarthanan, 2023. "Influence of pro-environmental behaviour towards behavioural intention of electric vehicles," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    5. Ekaterina A. Shashina & Valentina V. Makarova & Denis V. Shcherbakov & Tatiana S. Isiutina-Fedotkova & Nadezhda N. Zabroda & Nina A. Ermakova & Anton Yu. Skopin & Oleg V. Mitrokhin, 2021. "Use of Respiratory Protection Devices by Medical Students during the COVID-19 Pandemic," IJERPH, MDPI, vol. 18(11), pages 1-12, May.
    6. Muhammad Waqas Rana & Sufang Zhang & Shahid Ali & Iqra Hamid, 2022. "Investigating Green Financing Factors to Entice Private Sector Investment in Renewables via Digital Media: Energy Efficiency and Sustainable Development in the Post-COVID-19 Era," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    7. Bilal Ahmad & Da Liu & Mirza Huzaifa Asif & Muhammad Ashfaq & Muhammad Irfan, 2022. "Ambidextrous Leadership and Service Recovery Performance Under B2B Selling Context: An Examination Through Service Innovation Capability," SAGE Open, , vol. 12(2), pages 21582440221, May.
    8. Ekaterina A. Shashina & Ekaterina A. Sannikova & Denis V. Shcherbakov & Yury V. Zhernov & Valentina V. Makarova & Tatiana S. Isiutina-Fedotkova & Nadezhda N. Zabroda & Elena V. Belova & Nina A. Ermako, 2022. "Analysis of the Face Mask Use by Public Transport Passengers and Workers during the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(21), pages 1-13, November.
    9. Xiaojie Zhang & Lili Wang, 2022. "Factors Contributing to Citizens’ Participation in COVID-19 Prevention and Control in China: An Integrated Model Based on Theory of Planned Behavior, Norm Activation Model, and Political Opportunity S," IJERPH, MDPI, vol. 19(23), pages 1-18, November.
    10. Ziyuan Xie & Guixian Tian & Yongchao Tao, 2022. "A Multi-Criteria Decision-Making Framework for Sustainable Supplier Selection in the Circular Economy and Industry 4.0 Era," Sustainability, MDPI, vol. 14(24), pages 1-23, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zib:zbnaim:v:6:y:2022:i:2:p:43-46. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Zibeline International Publishing (email available below). General contact details of provider: https://actainformaticamalaysia.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.