The most impactful incident in the Globe in recent past and ongoing is the global pandemic post emergence of the virus (COVID-19). Pandemic has a dangerous effect on people and financial conditions around the world and the casualties are immeasurable. Public places are the main zone for such transmission. The World Health Organization (WHO) indicated the best way to stay safe from virus attack is to wear a face mask in crowded areas. As the country starts reopening various economic as well as social and educational activities, wearing face masks has become a vital and easy way of our regular lives for safety. To conduct business and perform social activities, wearing face masks will be an essential criterion. So to control the unconsciousness about wearing masks, the best solution is to give warning mask less people. In this project, we propose a method by using TensorFlow and OpenCV to identify face masks on individuals. An identifying bounding box is drawn over the face of the individual and defines whether somebody is wearing a mask or not. And according to proper detection a warning message will be generated and displayed on screen.
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28 November 2023
INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEM: (ICIS-2022)
17–18 September 2022
Agadir, Morocco
Research Article|
November 28 2023
Multimodal identification of face mask as a precautionary measure using digital image processing
Bappa Sutradhar;
Bappa Sutradhar
a)
1
Computer Science and Engineering, Swami Vivekananda University
, Kolkata, India
a)Corresponding Author:[email protected]
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Saswati Rakshit;
Saswati Rakshit
1
Computer Science and Engineering, Swami Vivekananda University
, Kolkata, India
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Somsubhra Gupta
Somsubhra Gupta
2
School of Computer Science, Swami Vivekananda University
, Kolkata, India
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a)Corresponding Author:[email protected]
AIP Conf. Proc. 2878, 020026 (2023)
Citation
Bappa Sutradhar, Saswati Rakshit, Somsubhra Gupta; Multimodal identification of face mask as a precautionary measure using digital image processing. AIP Conf. Proc. 28 November 2023; 2878 (1): 020026. https://doi.org/10.1063/5.0171365
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