The COVID-19 is a partner in Nursing unequaled disaster resulting in a huge range of casualties and protection issues. to cut back the unfold of coronavirus, individuals typically wear masks to guard themselves. This makes face popularity a truly tough project because bound components of the face rectangular measure hidden. A primary awareness of researchers for the duration of the continuing coronavirus pandemic is to come back up with hints to handle this downside thru fast and reasonably-priced solutions. during this paper, we tend to endorse a dependable technique supported by discard cloaked region and deep learning-based options to deal with the matter of the cloaked face recognition technique. the number one step is to discard the cloaked face vicinity. next, we tend to apply pre-trained deep Convolutional neural networks (CNN) to extract the only options from the received areas (in general eyes and forehead regions). in the end, the Bag-of-features paradigm is carried out on the function maps of the last convolutional layer to quantize them and to induce small illustration scrutiny to the simply related layer of classical CNN. in the end, Multilayer Perceptron (MLP) is implemented for the class approach. Experimental effects on real-global-Masked-Face-Dataset display high popularity overall performance.
Cite this article:
Hema Malini S. Efficient Cloaked Face Recognition Methodology throughout The Covid-19 Pandemic. Research Journal of Engineering and Technology. 2021;12(3):85-9. doi: 10.52711/2321-581X.2021.00014
Hema Malini S. Efficient Cloaked Face Recognition Methodology throughout The Covid-19 Pandemic. Research Journal of Engineering and Technology. 2021;12(3):85-9. doi: 10.52711/2321-581X.2021.00014 Available on: https://ijersonline.org/AbstractView.aspx?PID=2021-12-3-5
1- Xiaoming Peng, Mohammed Bennamoun, and Ajmal S Mian. A training-free nose tip detection method from face range images. Pattern Recognition, 44(3):544–558, 2011.
2- Xiaoguang Lu, Anil K Jain, and Dirk Colbry. Matching 2.5 d face scans to 3d models. IEEE transactions on pattern analysis and machine intelligence, 28(1):31–43, 2005.
3- Melissa L Koudelka, Mark W Koch, and Trina D Russ. A pre-screener 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.
4- Aleix M Martínez. 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.
5- Renliang Weng, Jiwen Lu, and Yap-Peng Tan. Robust point set matching for partial face recognition. IEEE transactions on image processing, 25(3):1163–1176, 2016.
6- Yueqi Duan, Jiwen Lu, Jianjiang Feng, and Jie Zhou. Topology preserving structural matching for automatic partial face recognition. IEEE Transactions on Information Forensics and Security, 13(7):1823–1837, 2018.
7- Niall McLaughlin, Ji Ming, and Danny Crookes. Largest matching areas for illumination and occlusion robust face recognition. IEEE transactions on cybernetics, 47(3):796–808, 2016.
8- Parama Bagchi, Debotosh Bhattacharjee, and Mita Nasipuri. Robust 3d face recognition in presence of pose and partial occlusions or missing parts. arXiv preprint arXiv:1408.3709, 2014.
9- Hassen Drira, Boulbaba Ben Amor, Anuj Srivastava, Mohamed Daoudi, and Rim Slama. 3d face recognition under expressions, occlusions, and pose variations. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(9):2270–2283, 2013.
10- Ashwini S Gawali and Ratnadeep 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.
11- G Nirmala Priya and RSD Wahida Banu. Occlusion invariant face recognition using mean-based weight matrix and support vector machine. Sadhana, 39(2):303–315, 2014.
12- Nese Alyuz, Berk Gokberk, and Lale Akarun. 3-d face recognition under occlusion using masked projection. IEEE Transactions on Information Forensics and Security, 8(5):789–802, 2013.
13- Xun Yu, Yongsheng Gao, and Jun Zhou. 3d face recognition under partial occlusions using radial strings. In 2016 IEEE International Conference on Image Processing (ICIP), pages 3016–3020. IEEE, 2016.
14- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012.
15- Lingxiao He, Haiqing Li, Qi Zhang, and Zhenan Sun. Dynamic feature matching for partial face recognition. IEEE Transactions on Image Processing, 28(2):791–802, 2018.
16- Lingxue Song, Dihong Gong, Zhifeng Li, Changsong Liu, and Wei Liu. Occlusion robust face recognition based on mask learning with the pairwise differential siamese network. In Proceedings of the IEEE International Conference on Computer Vision, pages 773–782, 2019.
17- Zhongyuan Wang, Guangcheng Wang, Baojin Huang, Zhangyang Xiong, Qi Hong, Hao Wu, Peng Yi, Kui Jiang, Nanxi Wang, Yingjiao Pei, et al. Masked face recognition dataset and application. arXiv preprint arXiv:2003.09093, 2020.
18- Nizam Ud Din, Kamran Javed, Seho Bae, and Juneho Yi. A novel gan-based network for the unmasking of the masked face. IEEE Access, 8:44276–44287, 2020.