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Approach to Prediction of Unmasked Face from Masked Face using Deep Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Apoorva Shete, Rachaell Nihalaani, Amit Hatekar

Apoorva Shete, Rachaell Nihalaani and Amit Hatekar. Approach to Prediction of Unmasked Face from Masked Face using Deep Learning. International Journal of Computer Applications 183(36):16-19, November 2021. BibTeX

	author = {Apoorva Shete and Rachaell Nihalaani and Amit Hatekar},
	title = {Approach to Prediction of Unmasked Face from Masked Face using Deep Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2021},
	volume = {183},
	number = {36},
	month = {Nov},
	year = {2021},
	issn = {0975-8887},
	pages = {16-19},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2021921745},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Due to the outbreak of COVID-19, it has become mandatory for each and every person to step outside only with a face mask on. This has raised the security and safety concerns among people as faces of criminals, burglars, etc are not recognisable through the CCTVs and security cameras. This problem can be tackled with the help of deep learning. In this paper, a model that can predict the unmasked face of a person from a masked face input image, giving an unmasked face image as the output was implemented. The accuracy achieved by the model is 91%. This paper ends with a review of the model’s usefulness and its scope for further development and improved results in the future.


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Machine Learning, Deep Learning, Autoencoders, Face Mask, Unmasking