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

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 36
Year of Publication: 2021
Authors:
Apoorva Shete, Rachaell Nihalaani, Amit Hatekar
10.5120/ijca2021921745

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

@article{10.5120/ijca2021921745,
	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 = {http://www.ijcaonline.org/archives/volume183/number36/32162-2021921745},
	doi = {10.5120/ijca2021921745},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

  1. N. Ud Din, K. Javed, S. Bae and J. Yi, "A Novel GAN-Based Network for Unmasking of Masked Face," in IEEE Access, vol. 8, pp. 44276-44287, 2020, doi: 10.1109/ACCESS.2020.2977386.
  2. F. Bhusra, A. Jhapate.(2021) RESCNN: A Deep Learning Approach for Unmasking Face Masks. Smart Moves Journal, ISSN NO: 2582-4600. [Online]. [Available]:
  3. https://ijoscience.com/ojsscience/index.php/ojsscience/article/view/350/815
  4. X. Wu, F.L. Zhang, J. Wang, et al. (2019). Deep Portrait Image Completion and Extrapolation. [Available]: https://arxiv.org/pdf/1808.07757.pdf
  5. S. Iizuka, E. Simo-Serra and H. Ishikawa, "Globally and locally consistent image completion", ACM Trans. Graph., vol. 36, no. 4, pp. 1-14, Jul. 2017.
  6. R. R. Shetty, M. Fritz and B. Schiele, "Adversarial scene editing: Automatic object removal from weak supervision", Proc. Adv. Neural Inf. Process. Syst., pp. 7706-7716, 2018.
  7. J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu and T. S. Huang, "Generative image inpainting with contextual attention", Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., pp. 5505-5514, Jun. 2018.
  8. K. Nazeri, E. Ng, T. Joseph, F. Z. Qureshi and M. Ebrahimi, "EdgeConnect: Generative image inpainting with adversarial edge learning", arXiv:1901.00212, 2019, [online] Available: http://arxiv.org/abs/1901.00212.
  9. M. K. J. Khan, N. Ud Din, S. Bae and J. Yi, "Interactive removal of microphone objects in facial images", Electronics, vol. 8, no. 10, pp. 1115, Oct. 2019.
  10. “Dataset”, https://www.kaggle.com/prasoonkottarathil/face-mask-lite-dataset

Keywords

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