A practical implementation of mask detection for COVID-19 using face detection and histogram of oriented gradients

被引：0

作者：

Chelbi S. ^{[1
]}

Mekhmoukh A. ^{[2
]}

机构：

[1] Geni-electric Departement, University of Bouira

[2] Faculté de Technologie, Département Automatique, Télécommunication et Electronique, Laboratoire de Technologie Industrielle et de l’Information, Université de Bejaia

来源：

Australian Journal of Electrical and Electronics Engineering | 2022年 / 19卷 / 02期

关键词：

face detection; HOG descriptor; KNN algorithm; mask detection; object recognition; SVM classifier; Viola-Jones algorithm;

D O I：

10.1080/1448837X.2021.2023071

中图分类号：

学科分类号：

摘要：

Wearing a face mask is one of the effective barriers against the coronavirus COVID-19 pandemic. It offers protection according to the World Health Organization and many medical papers. This paper proposes a method for masked face recognition in order to force the population to put on masks and reduce the COVID-19 pandemic in the world. The Viola-Jones algorithm is used to detect the face, and the Histogram of Oriented Gradients (HOG) technique was used to extract the relevant features from face images. The performance of the proposed algorithm is analysed for different data using two common image classification methods, including support vector machines and K Nearest Neighbor (KNN) algorithm for machine learning, which are used to classify the feature vectors. Their performance was compared and evaluated using accuracy. In this case, the experimental result shows that the support vector machine classifier achieved the highest accuracy and surpasses the KNN method in mask detection with an accuracy of 99.43%. ©, Engineers Australia.

引用

页码：129 / 136

页数：7

共 34 条

[1]

Ahn S., Park J., Chong J., Blurring Image Quality Assessment Method Based on Histogram of Gradient, Proceedings of the 19th Brazilian Symposium on Multimedia and the Web (WebMedia ‘13), pp. 181-184, (2013)

[2]

Altay O., Ulas M., Prediction of the Autismspectrum Disorder Diagnosis with Linear Discriminantanalysis Classifier and K-nearest Neighbor in Children, In 2018 6th International Symposium on Digital orensicand Security (ISDFS), pp. 1-4, (2018)

[3]

Bishop C.M., Pattern Recognition and Machine Learning, (2006)

[4]

Bremner D., Demaine E., Erickson J., Iacono J., Langerman S., Morin P., Toussaint G., Outputsensitive Algorithms for Computing Nearest-neighbor Decision Boundaries, Discrete & Computational Geometry, 33, 4, pp. 593-604, (2005)

[5]

Cortes C., Vapnik V., Support-vector Networks, Machine learning, 20, 3, pp. 273-297

[6]

Cover T., Hart P., Nearest-neighbor Pattern Classification Information Theory, IEEE Transactions, 13, pp. 21-27, (1967)

[7]

Dalai N., Triggs B., Histograms of Oriented Gradients for Human Detection, IEEE Conference on Computer Vision and Pattern Recognition, 1, pp. 886-893, (2005)

[8]

Dalal N., Triggs B., Histograms of Oriented Gradients for Human Detection, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ‘05), pp. 886-893, (2005)

[9]

Deniz O., Bueno G., Salido J., De la Torre F., Face Recognition Using Histograms of Oriented Gradients, Pattern Recognition Letters, 32, 12, pp. 1598-1603, (2011)

[10]

Doyle S.A., Madabhushi A., Feldman M., Tomaszewski J., Automated Grading of Breast Cancer Histopathology Using Spectralclustering with Textural and Architectural Image Features, 5thIEEE International Symposium on Biomedical Imaging: From Nanoto Macro, pp. 496-499, (2008)

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