Abstract
Nowadays, wearing a face mask is a vital routine in life, but threats are increasing in public due to the advantage of wearing face masks. Existing works do not perfectly detect the human face and also not possible to apply for different faces detection. To overwhelm this issue, in this paper we proposed real-time face mask detection. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, and face mask detection. In the first step, a set of keyframes are selected using the histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (parameterized CLAHE), and pose normalization (Angular Affine Transformation). In the third step, facial parts are segmented using the clustering approach i.e., Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, CapsNet based Feature Extraction is performed using CapsNet approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. Experiments conducted using Python IDLE 3.8 for the proposed model and also previous works as GMM with Deep learning (GMM + DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheng, V.C.C., Wong, S.-C., Chuang, V.W.M., So, S.Y.C., Chen, J.H.K., Sridhar, S., Yuen, K.-Y., et al.: The role of community-wide wearing of face mask for control of coronavirus disease 2019 (COVID-19) epidemic due to SARS-CoV-2. J. Infect. (2020)
Cabani, A., Hammoudi, K., Benhabiles, H., Melkemi, M.: MaskedFace-Net—a dataset of correctly/incorrectly masked face images in the context of COVID-19. Smart Health 100144 (2020)
Razavi, M., Alikhani, H., Janfaza, V., Sadeghi, B., Alikhani, E.: An automatic system to monitor the physical distance and face mask wearing of construction workers in COVID-19 pandemic (2021)
Ejaz, M.S., Islam, M.R.: Masked face recognition using convolutional neural network. In: 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (2019)
Meenpal, T., Balakrishnan, A., Verma, A.: Facial mask detection using semantic segmentation. In: 2019 4th International Conference on Computing, Communications and Security (ICCCS) (2019)
Bhuiyan, M.R., Khushbu, S.A., Islam, M.S.: A deep learning based assistive system to classify COVID-19 face mask for human safety with YOLOv3. In: 2020 11th international conference on computing, communication and networking technologies (ICCCNT) (2020)
Bu, W., Xiao, J., Zhou, C., Yang, M., Peng, C.: A cascade framework for masked face detection. In: 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM) (2017)
Joshi, A.S., Joshi, S.S., Kanahasabai, G., Kapil, R., Gupta, S.: Deep learning framework to detect face masks from video footage. In: 2020 12th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 435–440 (2020)
Draughon, G., Sun, P., Lynch, J.: Implementation of a computer vision framework for tracking and visualizing face mask usage in urban environments. In: 2020 IEEE International Smart Cities Conference (ISC2), 1–8 (2020)
Kose, N., Dugelay, J.-L.: Mask spoofing in face recognition and countermeasures. Image Vis. Comput. 32(10), 779–789 (2014)
Qezavati, H., Majidi, B., Manzuri, M.T.: Partially covered face detection in presence of headscarf for surveillance applications. In: 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), pp. 195–199 (2019)
Yuan, C., Yang, Q.: A dynamic face recognition deploy and control system based on deep learning. J. Residuals Sci. Technol. 13 (2016)
Engoor, S., Selvaraju, S., Christopher, H.S., Suryanarayanan, M.G., Ranganathan, B.: Effective emotion recognition from partially occluded facial images using deep learning (2020)
Salari, S.R., Rostami, H.: Pgu-face: a dataset of partially covered facial images. Data Brief 9, 288–291 (2016)
Song, L., Gong, D., Li, Z., Liu, C., Liu, W.: Occlusion robust face recognition based on mask learning W ith pairwise differential Siamese network. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 773–782 (2019)
Wang, Z., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., Yi, P., Jiang, K., Wang, N., Pei, Y., Chen, H., Miao, Y., Huang, Z., Liang, J.: Masked face recognition dataset and application. ArXiv abs/2003.09093 (2020)
Nair, A., Potgantwar, A.: Masked face detection using the Viola Jones algorithm: a progressive approach for less time consumption. Int. J. Recent Contrib. Eng. Sci. IT 6, 4–14 (2018)
Ejaz, M.S., Islam, M.N., Sifatullah, M., Sarker, A.: Implementation of principal component analysis on masked and non-masked face recognition. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp. 1–5 (2019)
Hariri, W.: Efficient masked face recognition method during the COVID-19 pandemic (2020)
Dey, S.K., Howlader, A., Deb, C.: MobileNet mask: a multi-phase face mask detection model to prevent person-to-person transmission of SARS-CoV-2 (2021)
Loey, M., Manogaran, G., Taha, M., Khalifa, N.E.: Fighting against COVID-19: a novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection. Sustain. Cities Soc. 65, 102600 (2020)
Nagrath, P., Jain, R., Madan, A., Arora, R., Kataria, P., Hemanth, J.D.: SSDMNV2: a real-time DNN-based face mask detection system using single shot multibox detector and MobileNetV2. Sustain. Cities Soc. (2020)
Chowdary, G.J., Punn, N.S., Sonbhadra, S.K., Agarwal, S.: Face mask detection using transfer learning of inceptionV3. ArXiv abs/2009.08369 (2020)
Sikandar, T., Samsudin, W.N.A.W., Rabbi, M.F., Ghazali, K.H.: An efficient method for detecting covered face scenarios in ATM surveillance camera. SN Comput. Sci. 1(3) (2020)
Loey, M., Manogaran, G., Taha, M., & Khalifa, N.: A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic. Measurement 167, 108288 (2021)
Chen, Q., Sang, L.: Face-mask recognition for fraud prevention using gaussian mixture model. J. Vis. Commun. Image Representation 55 (2018)
Kim, M., Koo, J., Cho, S., Baek, N., Park, K.: Convolutional neural network-based periocular recognition in surveillance environments. IEEE Access 1–1 (2018)
Liu, D., Bellotto, N., Yue, S.: Deep spiking neural network for video-based disguise face recognition based on dynamic facial movements. IEEE Trans. Neural Netw. Learn. Syst. 1–10 (2019)
Ud Din, N., Javed, K., Bae, S., Yi, J.: A novel GAN-based network for unmasking of masked face. IEEE Access 8, 44276–44287 (2020)
Zhao, Z., Kumar, A.: Improving periocular recognition by explicit attention to critical regions in deep neural network. IEEE Trans. Inf. Forensics Secur. 13(12), 2937–2952 (2018)
Zhang, W., Zhao, X., Morvan, J.-M., Chen, L.: Improving shadow suppression for illumination robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1–1 (2018)
l-Shaibani, B.: A new fast local Laplacian completed local ternary count (FLL-CLTC) for facial image classification. IEEE Access 8, 98244–98254 (2020)
Acknowledgements
This work was supported by the National Science Fund of Bulgaria: KP-06-H27/16 Development of efficient methods and algorithms for tensor-based processing and analysis of multidimensional images with application in interdisciplinary areas, National Science Foundation under Award No. OIA-1946391(DART) and NSA grant NCAE-C Cyber Curriculum and Research 2020 Program, NCAEC-003-3030
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Nasiri, E., Milanova, M., Nasiri, A. (2022). Masked Face Detection Using Artificial Intelligent Techniques. In: Kountchev, R., Mironov, R., Nakamatsu, K. (eds) New Approaches for Multidimensional Signal Processing. Smart Innovation, Systems and Technologies, vol 270. Springer, Singapore. https://doi.org/10.1007/978-981-16-8558-3_1
Download citation
DOI: https://doi.org/10.1007/978-981-16-8558-3_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8557-6
Online ISBN: 978-981-16-8558-3
eBook Packages: EngineeringEngineering (R0)