Detection of Face-Mask in Real Time: A Cascaded Bi-Level Feature Extraction Technique Approach
DOI:
https://doi.org/10.58190/ijamec.2023.66Abstract
Due to COVID-19's rapid spread, millions of people around the world have been affected and there has been extensive destruction. One of the most effective ways of preventing its spread and transmission during the pandemic period wearing of a mask and was required then in most public areas. As a result, this necessitate the use of programmed real-time mask detection devices in place of manual reminders. Face mask detection requires real-time processing of a huge amount of data with constrained processing resources, hence local descriptors that are quick to calculate, quick to match, and cheap to store are highly sought after. To achieve improved matching and reduction in memory use and computational complexity, the study offers a combination of Features from Accelerated Segment Test (FAST) corner detector with Histogram of Oriented Gradient (HOG) feature descriptor to allow faster matching and minimize memory usage and computation cost. The features obtained were then classified into face mask present and face mask absent using SVM, NB and CNN. Results obtained gives an accuracy of 99.41% which was higher than that previous results of 99.27% and 95% accuracy. Furthermore, it took the system only 48secs to extract the features obtained from face for training and testing. This outcome confirmed the suitability of the suggested method for real-time face mask detection.
Downloads
References
F. Shahid, A. Zameer, and M. Muneeb, “Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM,” Chaos Solitons Fractals, vol. 140, p. 110212, 2020, doi: 10.1016/j.chaos.2020.110212.
X. Jiang, T. Gao, Z. Zhu, and Y. Zhao, “Real-time face mask detection method based on yolov3,” Electronics (Switzerland), vol. 10, no. 7, Apr. 2021, doi: 10.3390/electronics10070837.
X. Fan and M. Jiang, “RetinaFaceMask: A Single Stage Face Mask Detector for Assisting Control of the COVID-19 Pandemic,” May 2020, [Online]. Available: http://arxiv.org/abs/2005.03950
M. Loey, G. Manogaran, M. H. N. Taha, and N. E. M. Khalifa, “A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic,” Measurement (Lond), vol. 167, Jan. 2021, doi: 10.1016/j.measurement.2020.108288.
M. K. Tripathi and D. D. Maktedar, “A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields : A survey,” Information Processing in Agriculture, no. xxxx, 2019, doi: 10.1016/j.inpa.2019.07.003.
M. M. Boulos, “Facial Recognition and Face Mask Detection Using Machine Learning Techniques,” 2021. [Online]. Available: https://digitalcommons.montclair.edu/etdhttps://digitalcommons.montclair.edu/etd/728
Ejaz Sabbir and Islam Rabiul, “Masked Face Recognition Using Convolutional Neural Network,” in International Conference on Sustainable Technologies for Industry 4.0 (STI), 2019.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” Proceedings of the IEEE International Conference on Computer Vision, no. May, pp. 2564–2571, 2011, doi: 10.1109/ICCV.2011.6126544.
E. Karami, S. Prasad, and M. Shehata, “Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images,” 2017, [Online]. Available: http://arxiv.org/abs/1710.02726
E. Oyallon and J. Rabin, “An Analysis of the SURF Method,” Image Processing On Line, vol. 5, no. 2004, pp. 176–218, 2015, doi: 10.5201/ipol.2015.69.
B. B. Swapnali and K. S. Vijay, “Feature Extraction Using Surf Algorithm for Object Recognition,” International Journal of Technical Research and Applications, vol. 2, no. 4, pp. 197–199, 2014, [Online]. Available: www.ijtra.com
B. Zohuri, “Deep Learning Limitations and Flaws,” Modern Approaches on Material Science, vol. 2, no. 3, Jan. 2020, doi: 10.32474/mams.2020.02.000138.
J. Wang, Y. Yuan, and G. Yu, “Face Attention Network: An Effective Face Detector for the Occluded Faces,” Computer Vision and Pattern Recognition , Nov. 2017, [Online]. Available: http://arxiv.org/abs/1711.07246
A. Cabani, K. Hammoudi, H. Benhabiles, and M. Melkemi, “MaskedFace-Net – A dataset of correctly/incorrectly masked face images in the context of COVID-19,” Smart Health, vol. 19, Mar. 2021, doi: 10.1016/j.smhl.2020.100144.
Tomas Jesus, Rego Albert, Viciano-Tudela Sandra, and Lioret Jaime, “Incorrect Facemask-Wearing Detection Using Convolutional Neural Networks with Transfer Learning,” Healthcare, vol. 9, no. 1050, 2021, doi: 10.3390/healthcare9081050.
Z. Wang et al., “Masked Face Recognition Dataset and Application,” Mar. 2020, [Online]. Available: http://arxiv.org/abs/2003.09093
A. Alzu’bi, F. Albalas, T. Al-Hadhrami, L. B. Younis, and A. Bashayreh, “Masked face recognition using deep learning: A review,” Electronics (Switzerland), vol. 10, no. 21. MDPI, Nov. 01, 2021. doi: 10.3390/electronics10212666.
U. Scherhag, C. Rathgeb, J. Merkle, R. Breithaupt, and C. Busch, “Face Recognition Systems Under Morphing Attacks : A Survey,” vol. 7, 2019.
Y.-Q. Wang, “An Analysis of the Viola-Jones Face Detection Algorithm,” Image Processing On Line, vol. 4, pp. 128–148, 2014, doi: 10.5201/ipol.2014.104.
P. ; Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” 2004. [Online]. Available: http://www.merl.com
C. Saravanan, “Color image to grayscale image conversion,” 2010 2nd International Conference on Computer Engineering and Applications, ICCEA 2010, vol. 2, no. April 2010, pp. 196–199, 2010, doi: 10.1109/ICCEA.2010.192.
R. Verma, M. Rohit Verma, and J. Ali, “A comparative study of various types of image noise and efficient noise removal techniques,” 2013. [Online]. Available: www.ijarcsse.com
F. Iftikhar and J. Mohammed, “Algorithm for Image Processing Using Improved Eliminat ion of Gaussian Noise from FPGA Based Co-Processors IJRES Journal An Improved Median Filt er Based on Efficient Noise Det ect ion for High Qualit y Image Rest orat ion,” 2011.
H. Zhang, J. Wohlfeil, and D. Grießbach, “EXTENSION and EVALUATION of the AGAST FEATURE DETECTOR,” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 3, pp. 133–137, 2016, doi: 10.5194/isprs-annals-III-4-133-2016.
A. v Kulkarni, J. S. Jagtap, and V. K. Harpale, “Object recognition with ORB and its Implementation on FPGA,” International Journal of Advanced Computer Research, no. 3, 2013.
M. Ghahremani, Y. Liu, and B. Tiddeman, “FFD: Fast Feature Detector,” IEEE Transactions on Image Processing, vol. 10, no. 10, Dec. 2020, doi: 10.1109/TIP.2020.3042057.
T. Surasak, I. Takahiro, C. H. Cheng, C. E. Wang, and P. Y. Sheng, “Histogram of oriented gradients for human detection in video,” Proceedings of 2018 5th International Conference on Business and Industrial Research: Smart Technology for Next Generation of Information, Engineering, Business and Social Science, ICBIR 2018, pp. 172–176, 2018, doi: 10.1109/ICBIR.2018.8391187.
J. J. Priyankha and K. Suresh, “Crop Disease Identification Using a Feature Extraction HOG Algorithm,” Asian Journal of Applied Science and Technology (AJAST), vol. 1, no. 3, pp. 35–39, 2017.
C. Shu, X. Ding, and C. Fang, “Histogram of the oriented gradient for face recognition,” Tsinghua Sci Technol, vol. 16, no. 2, pp. 216–224, 2011, doi: 10.1016/S1007-0214(11)70032-3.
K. J. Sreelekshmi and T. Y. Mahesh, “Human Identification Based on the Histogram of Oriented Gradients,” International Journal of Engineering Research & Technology (IJERT), vol. 3, no. 7, pp. 1611–1614, 2014.
M. K. Benkaddour and A. Bounoua, “Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition,” Traitement du Signal, vol. 34, no. 1–2, pp. 77–91, 2017, doi: 10.3166/TS.34.77-91.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, doi: 10.1038/nature14539.
A. Ferreira and G. Giraldi, “Convolutional Neural Network approaches to granite tiles classification,” Expert Syst Appl, vol. 84, pp. 1–11, 2017, doi: 10.1016/j.eswa.2017.04.053.
M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary Robust Independent Elementary Features,” 2010.
M. Kashif, T. M. Deserno, D. Haak, and S. Jonas, “Feature description with SIFT, SURF, BRIEF, BRISK, or FREAK? A general question answered for bone age assessment,” Comput Biol Med, vol. 68, pp. 67–75, Jan. 2016, doi: 10.1016/j.compbiomed.2015.11.006.
Y. Liu, H. Zhang, H. Guo, and N. N. Xiong, “A FAST-BRISK Feature Detector with Depth Information,” 2018, doi: 10.3390/s18113908.
R. Englund, “Machine Learning for Technical Information Quality Assessment,” no. March, 2016.
P. Walsh, “Support Vector Machine Learning for ECG Classification,” Smart Healthcare and Safety Systems, vol. 10, pp. 195–204, 2019.
J. Cao, M. Wang, Y. Li, and Q. Zhang, “Improved support vector machine classi cation algorithm based on adaptive feature weight updating in the Hadoop cluster environment,” pp. 1–12, 2020.
S. Ghosh, “A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification,” no. Iciss, pp. 24–28, 2019.
Y. Tang, “Deep Learning using Linear Support Vector Machines,” 2013.
V. Krishnaiah, G. Narsimha, and S. N. Chandra, “Heart Disease Prediction System Using Data Mining Technique by Fuzzy K-NN Approach,” in Advances in Intelligent Systems and Computing, 2015, pp. 371–384. doi: 10.1007/978-3-319-13728-5.
A. Kataria and M. D. Singh, “A Review of Data Classification Using K-Nearest Neighbour Algorithm,” International Journal of Emerging Technology and Advanced Engineering, vol. 3, no. 6, pp. 354–360, 2013.
H. Parvin, H. Alizadeh, and B. Minati, “A Modification on K-Nearest Neighbor Classifier,” Global Journal of Computer Science and Technology, vol. 10, no. 14, pp. 37–41, 2010.
J. Đ. Novakovic, A. Veljovic, and S. S. Ilic, “Experimental Study of using the K-Nearest Neighbour Classifier with Filter Methods,” Computer Science and Technologies, no. 451, pp. 90–99, 2016.
A. Sopharak et al., “Machine learning approach to automatic exudate detection in retinal images from diabetic patients,” J Mod Opt, vol. 57, no. 2, pp. 124–135, 2010, doi: 10.1080/09500340903118517.
D. Berrar, “Bayes ’ Theorem and Naive Bayes Classifier Bayes ’ Theorem and Naive Bayes Classifier,” Encyclopedia of Bioinfor- matics and Computational Biology, no. January 2018, pp. 0–18, 2019, doi: 10.1016/B978-0-12-809633-8.20473-1.
B. Harangi, B. Antal, and A. Hajdu, “Automatic exudate detection with improved naïve-Bayes classifier,” Proc IEEE Symp Comput Based Med Syst, 2012, doi: 10.1109/CBMS.2012.6266341.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 International Journal of Applied Methods in Electronics and Computers
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
