Masked Face Recognition Using MobileNetV2
Authors: 
Ming Liu, Wei Qi YanAuthors Info & Claims
Ming Liu, Wei Qi YanAuthors Info & ClaimsPages 134 - 138
Abstract
Masked face recognition has made great progress in the field of computer vision since the popularity of COVID-19 epidemic in 2020. In countries with severe outbreaks, people are required to wear masks in public. The current face recognition methods, which take use of the whole face as input data, are quite well established. However, while people are use of face masks, it will reduce the accuracy of face recognition. Therefore, we propose a mask wearing recognition method based on MobileNetV2 and solve the problem that many of models cannot be applied to portable devices or mobile terminals. The results indicate that this method has 98.30% accuracy in identifying the masked face. Simultaneously, a higher accuracy is obtained compared to VGG16. This approach has proven to be working well for the practical needs.
References
[1]
Asadi S., Cappa C., Wexler. A. Aerosol emission and superemission during human speech increase with voice loudness. Scientific Reports, pp. 1–10. (2019)
[2]
Kaiming, H., Shao, Q.R., Xiang, Y.Z. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, pp. 102-108 (2015)
[3]
Jamadar, S., Joshi, P., Surve, M., Vharkate, M. ZIB automatic attendance system using face recognition technique. J. Recent Technol. Eng, vol. 9, no. 1, pp. 2134–2138. Sciences Publication (2020)
[4]
Adjabi, I., Benzaoui, A., Ouahabi, A. Past, present and future of face recognition: A review. Electronics, vol.9, no.8, pp. 1-53. (2020)
[5]
Aswal, V., Charniya, N., Shaikh, S., Tupe, O. Single camera masked face. IEEE International Seminar on Research of Information Technology and Intelligent Systems, pp. 57–60. (2020)
[6]
Arymurthy, A., Gultom, Y., Masikome, J. Batik classification using deep convolutional network transfer. Journal Ilmu Komputer Dan Informasi, vol. 11, no. 2, pp. 59 (2018)
[7]
Goh, Y.H., Lee, Y.B., Lum, K.Y. American sign language recognition based on MobileNetV2. Adv.Sci.Technol, vol. 5, no. 6, pp. 481-488. ASTES (2020)
[8]
Andrew, G., Marco, A., Weijun, W., Weyand, T. MobileNets: Efficient convolutional neural networks for mobile vision applications. NASA, vol.12, pp 233-240 (2017)
[9]
Elmahmudi, A., Ugail, H. Deep face recognition using imperfect facial data. Futur.Gener.Comput.Syst., vol. 99, pp. 213-225, Springer (2019)
[10]
Hu, L., Ge, Q. Automatic facial expression recognition based on MobileNetV2 in real-time. Phys, J, vol. 15449, no. 2, IOP science (2020)
[11]
Cai, Q., Peng, C., Shi, X. Lightweight face recognition algorithm based on MobileNetV2. International Journal of Intelligence Science, Vol.11 No.1 (2021)
[12]
Agrawal, A., Choudhary, A. Gopalakrishnan, K., Khaitan, K.: Deep convolutional neural networks with transfer learning for computer vision-based data-driven pavement distress detection. Constr,Buid.Matter., vol. 157, pp. 322-330, Springer (2017)
[13]
Li, M., Liu, W., Wen, Y., Yu, Z. Sphere face: Deep hypersphere embedding for face recognition. IEEE Conference on Computer Vision and Pattern Recognition, pp. 6738-6746. (2017)
[14]
Deng, J., Guo, J., Xue, N., Yang, J. ArcFace additive angular margin loss for deep face recognition. IEEE International Conference on Computer Vision, Visual Communications and Image Processing, vol. 124, no.1, pp 133-137 (2016)
[15]
Zhang, T. Adaptive forward-backward greedy algorithm for learning sparse representations. IEEE Transactions on Information Theory, pp. 4689-4708. (2011)
[16]
Ahuja, U., Kumar, K., Kumar, M., Sachdeva, M., Singh, S. Face mask detection using YOLO3 and faster R-CNN models: COVID-19 Environment, pp. 19753-19768, Springer (2021)
[17]
Akhil, K., Kalia, A., Kaushal, M., Sharma, A. A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system. Journal of Ambient Intelligence Humanized Computing, vol. 14752, no.5, pp 142-145 (2021)
[18]
Lahasan, B., Lutfi, S.L., Segundo, R. A survey on techniques to handle face recognition challenges: Occlusion, single sample per subject and expression. Artificial Intelligence Review, pp. 949-979, Springer (2019)
[19]
Albanie, S., Hu, J., Shen, L., Sun, G., Wu, E. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 8, pp. 2011-2023, IEEE (2019)
[20]
Chen, L., Howard, A., Sandler, M., Zhmoginov, A., Zhu, M. MobileNetv2: Inverted residuals and linear bottlenecks. IEEE Conference on Computer Vision and Pattern Recognition. Vol.142, no.7, pp 87-95 (2020)
[21]
Cabani, A., Benhabiles,H., Hammoudi, K., Melkemi, M. Masked face-net-A dataset of correctly/incorrectly masked face images in the context of COVID-19. Smart Health, vol. 19, pp 31-40 (2020)
[22]
Wei Qi Yan. Computational Methods for Deep Learning Theoretic, Practice and Applications, Springer (2021)
[23]
Wei Qi Yan. Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics, Springer (2019)
[24]
Xinyi Gao, Minh Nguyen, Wei Qi Yan. Face image inpainting based on generative adversarial network. International Conference on Image and Vision Computing New Zealand (2021)
[25]
Wang Hui. Real-time Face Detection and Recognition Based on Deep Learning (Masters Thesis), Auckland University of Technology (2018).
[26]
Yanzhao Zhu, Wei Qi Yan. Traffic sign recognition based on deep learning. Multimedia Tools and Applications, Springer.
[27]
Qin Gu, Jianyu Yang, Lingjiang Kong, Wei Qi Yan; Reinhard Klette. Embedded and real-time vehicle detection system for challenging on-road scenes. Optical Engineering 56 (6), 063102, pp 14-25 (2017).
[28]
Analia R., Karlina I., Susanto S. The face mask detection for preventing the spread of COVID-19 at Politeknik. IEEE International Conference on Applied Engineering (ICAE) pp. 115–124. (2020)
[29]
Jia Z, Yang W. Real-time face detection based on YOLO. IEEE International Conference on Knowledge Innocation and Invention(ICKII), pp. 221-224 (2018)
[30]
Chenyang Wei., Dawei Yang., Yan Qi. An improved multi-target tracking method integrating visual tracking mechanism ICMLCA vol.12581, no.3, pp 112-150, 2022.
Index Terms
- Masked Face Recognition Using MobileNetV2
Recommendations
Local Eyebrow Feature Attention Network for Masked Face Recognition
During the COVID-19 coronavirus epidemic, wearing masks has become increasingly popular. Traditional occlusion face recognition algorithms are almost ineffective for such heavy mask occlusion. Therefore, it is urgent to improve the recognition performance ...
Masked Face Recognition with 3D Facial Geometric Attributes
IPMV '22: Proceedings of the 4th International Conference on Image Processing and Machine VisionDuring the coronavirus pandemic, the demand for contactless biometrics technology has promoted the development of masked face recognition. Training a masked face recognition model needs to address two crucial issues: a lack of large-scale realistic ...
Masked Face Recognition with Generative Data Augmentation and Domain Constrained Ranking
MM '20: Proceedings of the 28th ACM International Conference on MultimediaMasked faces recognition (MFR) aims to match a masked face with its corresponding full face, which is an important task especially during the global outbreak of COVID-19. However, most existing face recognition models generalize poorly in this case, and ...
Comments
Information & Contributors
Information
Published In
August 2022
241 pages
ISBN:9781450397315
DOI:10.1145/3561613
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 November 2022
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICCCV 2022
ICCCV 2022: 2022 The 5th International Conference on Control and Computer Vision
August 19 - 21, 2022
Xiamen, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 71Total Downloads
- Downloads (Last 12 months)10
- Downloads (Last 6 weeks)1
Reflects downloads up to 05 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format