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

[21]

O.Connor B., Kaushi R., Facial Recognition Using Modified Local Binary Patternand Random Forest, International Journal of Artificial Intelligence & Applications, 4, (2013)

[22]

Park J.-S., Oh Y.H., ChulAhn S., Lee S.-W., Glasses Removal from Facial Image Using Recursive Error Compensation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 5, pp. 805-811, (2005)

[23]

Face Detection Using Viola Jones Algorithm and Neural Networks, 2018 Fourth International Conference on Computing 21. Communication Control and Automation (ICCUBEA), pp. 1-6, (2018)

[24]

Qin B., Li D., Identifying Facemask-wearing Condition Using Image Super-resolution with Classification Network to Prevent COVID-19, 20, (2020)

[25]

Rowley H.A., Balujia S., Kanade T., Neural Network-based Face Detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1, pp. 23-38, (1998)

[26]

Ruvinga C., Malathi D., DorathiJayaseeli J.D., Exploration of Face Detection Methods in Digital Images, International Journal of Recent Technology and Engineering (IJRTE), 8, 4, pp. 12130-12136, (2019)

[27]

Shamia D., Chandy D.A., Analyzing the Performance of Viola Jones Face Detector on the LDHF Database, 2017 International Conference on Signal Processing and Communication(ICSPC), 30, pp. 312-315, (2017)

[28]

Su C.-Y., Yang J.-F., Histogram of Gradient Phases: A New Local Descriptor for Face Recognition, IET Computer Vision, 8, 6, pp. 556-567, (2014)

[29]

Torrione P., Morton K.D., Sakaguchi R., Collins L.M., Histogram of Gradient Features for Buried Threat Detection in Ground Penetrating Radar Data, Proceedings of the 32nd IEEE International Geoscience and Remote Sensing Symposium (IGARSS ‘12), pp. 3182-3185, (2012)

[30]

Viola P., Jones M., Rapid Object Detection Using a Boosted Cascade of Simple Features, Proceeding of International Conference on Computer Vision and Pattern Recognition (CVPR), (2001)

← 1 2 3 4 →