Face Mask Detection– A Machine Learning Approach

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Mounusha S
Dr. Mallikarjun M kodabagi

Abstract

The COVID-19 is an unmatched emergency inciting an immense number of incidents and security issues. To reduce the spread of Covid, individuals regularly wear shroud to promise themselves. This makes the face attestation an especially infuriating undertaking since unequivocal pieces of the face are hidden. A basic mark of the intermingling of specialists during the progressing Covid pandemic is viewed as plans to deal with this issue through fast and suitable strategies. Face Detection has made a remarkable issue in Image preparation and Computer Vision. Different new figurings are being envisioned utilizing convolutional developments to make the most of them as exact as could be viewed as ordinary. These convolutional models have made it conceivable to eliminate even the pixel subtleties. We desire to plan an equivalent face classifier that can perceive any face present in the bundling paying little psyche to its course of action. Beginning from the RGB picture of any size, the method utilizes Predefined Training Weights of Architecture with arranging is performed through Fully Convolutional Networks. This is to correspondingly set up to see an unmistakable facial cover in a solitary edge.

 

 

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References

Alyuz, B. Gokberk, and L. Akarun. 3-d face recognition under occlusion using masked projection. IEEE Transactions on Information Forensics and Security, 8(5):789–802, 2013.

Bagchi, D. Bhattacharjee, and M. Nasipuri. Robust 3d face recognition in presence of pose and partial occlusions or missing parts.arXiv preprint arXiv:1408.3709, 2014.

U. Din, K. Javed, S. Bae, and J. Yi. A novel gan-based network for unmasking of masked face. IEEE Access, 8:44276–44287, 2020.

Drira, B. Ben Amor, A. Srivastava, M. Daoudi, and R. Slama. 3d face recogni- tion under expressions, occlusions, and pose variations. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(9):2270–2283, 2013.

Duan, J. Lu, J. Feng, and J. Zhou. Topology preserving structural matching for automatic partial face recognition. IEEE Transactions on Information Forensics and Security, 13(7):1823–1837, 2018.

S. Gawali and R. R. Deshmukh.3d face recognition using geodesic facial curves to handle expression, occlusion and pose variations. International Journal of Computer Science and Information Technologies, 5(3):4284–4287, 2014.

He, H. Li, Q. Zhang, and Z. Sun. Dynamic feature matching for partial face recog- nition. IEEE Transactions on Image Processing, 28(2):791–802, 2018.

E. King. Dlib-ml: A machine learning toolkit. The Journal of Machine Learning Research, 10:1755–1758, 2009.

Ashwinkumar.U.M and Dr.Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012

L. Koudelka, M. W. Koch, and T. D. Russ.A prescreener for 3d face recognition using radial symmetry and the hausdorff fraction. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops, pages 168–168. IEEE, 2005.

Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenetclassi􀂦cation with deep convo- lutional neural networks.In Advances in neural information processing systems, pages 1097–1105, 2012.

-C. Lian, Z. Li, B.-L. Lu, and L. Zhang. Max-margin dictionary learning for multiclass image categorization. In European Conference on Computer Vision, pages 157–170. Springer, 2010.

Lobel, R. Vidal, D. Mery, and A. Soto. Joint dictionary and classi􀂦er learning for categorization of images using a max-margin framework. In Paci􀂦c-Rim Symposium on Image and Video Technology, pages 87–98. Springer, 2013.

Loussaief and A. Abdelkrim. Deep learning vs. bag of features in machine learning for image classification. In 2018 International Conference on Advanced Systems and Electric Technologies (ICASET), pages 6–10. IEEE, 2018.

Lu, A. K. Jain, and D. Colbry. Matching 2.5 d face scans to 3d models. IEEE transactions on pattern analysis and machine intelligence, 28(1):31–43, 2005.

M. Martınez. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern analysis and machine intelligence, 24(6):748–763, 2002.