Design and Build of Masked Face Identification System and IoT-Based Body Temperature Measurement

I P.A. Bayupati, Aditya Ersapramana, I Putu Arya Dharmaadi


The new normal is an era in the behavior changed to obstruct the spread of COVID-19, such as decreasing people's mobility, body temperature measuring, mandatory masking, and getting a COVID-19 vaccine regularly. This study develops an identification system based on the Internet of Things through facial biometrics and temperature measurement. Face identification is divided into two main steps: face detection and identification. Face detections used the Framework YOLOv5, in which the systems can detect masked and without masked faces. Pre-trained VGG-face is used for face identification for feature extraction and produces a 2622-dimensional vector. The feature extraction result is calculated as the distance similarity with the features stored in the Database using Euclidean distance. Temperature measurement utilizes IoT by using the NodeMCU ESP8266 and the MLX90614 sensor. NodeMCU ESP8266 is a microcontroller equipped with a WI-FI module to send temperature data so measurements can be delivered wirelessly. The MLX90614 sensor measures body temperature at a 40 – 60 cm distance from the Sensor. Calibration of the sensor used Two-point Calibration, so a trim error rate level is produced. The result successfully identified the face with the F1 score of 92% without a masked face and 73% for a masked face. The body temperature was measured using the MLX90614 sensor produced an error rate of 0.1°C after calibration. In the future, this system can be further developed and utilized for other sectors, such as the medical and security sectors.


Face identification; feature extraction; internet of things; MLX90614; temperature measurement; YOLO; VGG-Face

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J. Bedford et al., “COVID-19: towards controlling of a pandemic,†Lancet, vol. 395, no. 10229, pp. 1015–1018, 2020, doi: 10.1016/S0140-6736(20)30673-5.

Y. C. Wu, C. S. Chen, and Y. J. Chan, “The outbreak of COVID-19: An overview,†J. Chinese Med. Assoc., vol. 83, no. 3, pp. 217–220, 2020, doi: 10.1097/JCMA.0000000000000270.

F. Albarello et al., “2019-novel Coronavirus severe adult respiratory distress syndrome in two cases in Italy: An uncommon radiological presentation,†Int. J. Infect. Dis., vol. 93, pp. 192–197, 2020, doi: 10.1016/j.ijid.2020.02.043.

X. Lv, L. Ding, and G. Zhang, “Research on fingerprint feature recognition of access control based on deep learning,†Int. J. Biom., vol. 13, no. 1, pp. 80–95, 2021, doi: 10.1504/IJBM.2021.112214.

S. Arya, N. Pratap, and K. Bhatia, “Future of Face Recognition: A Review,†Procedia Comput. Sci., vol. 58, pp. 578–585, 2015, doi: 10.1016/j.procs.2015.08.076.

A. Sepas-Moghaddam, A. Etemad, F. Pereira, and P. L. Correia, “CapsField: Light Field-Based Face and Expression Recognition in the Wild Using Capsule Routing,†IEEE Trans. Image Process., vol. 30, pp. 2627–2642, 2021, doi: 10.1109/TIP.2021.3054476.

M. Luo, J. Cao, X. Ma, X. Zhang, and R. He, “FA-GAN: Face Augmentation GAN for Deformation-Invariant Face Recognition,†IEEE Trans. Inf. Forensics Secur., vol. 16, pp. 2341–2355, 2021, doi: 10.1109/TIFS.2021.3053460.

J. Y. Choi and B. Lee, “Ensemble of Deep Convolutional Neural Networks with Gabor Face Representations for Face Recognition,†IEEE Trans. Image Process., vol. 29, no. c, pp. 3270–3281, 2020, doi: 10.1109/TIP.2019.2958404.

M. Awais et al., “Novel Framework: Face Feature Selection Algorithm for Neonatal Facial and Related Attributes Recognition,†IEEE Access, vol. 8, pp. 59100–59113, 2020, doi: 10.1109/ACCESS.2020.2982865.

H. Yang and X. Han, “Face recognition attendance system based on real-time video processing,†IEEE Access, vol. 8, pp. 159143–159150, 2020, doi: 10.1109/ACCESS.2020.3007205.

X. Li, Z. Yang, and H. Wu, “Face detection based on receptive field enhanced multi-task cascaded convolutional neural networks,†IEEE Access, vol. 8, pp. 174922–174930, 2020, doi: 10.1109/ACCESS.2020.3023782.

N. V. Kousik, Y. Natarajan, R. Arshath Raja, S. Kallam, R. Patan, and A. H. Gandomi, “Improved salient object detection using hybrid Convolution Recurrent Neural Network,†Expert Syst. Appl., vol. 166, p. 114064, 2021, doi: 10.1016/j.eswa.2020.114064.

S. Singh, U. Ahuja, M. Kumar, K. Kumar, and M. Sachdeva, “Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment,†Multimed. Tools Appl., 2021, doi: 10.1007/s11042-021-10711-8.

A. Kumar, A. Kalia, A. Sharma, and M. Kaushal, “A hybrid tiny YOLO v4-SPP module based improved face mask detection vision system,†J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2021, doi: 10.1007/s12652-021-03541-x.

R. Xu, H. Lin, K. Lu, L. Cao, and Y. Liu, “A forest fire detection system based on ensemble learning,†Forests, vol. 12, no. 2, pp. 1–17, 2021, doi: 10.3390/f12020217.

K. Lin et al., “Face Detection and Segmentation Based on Improved Mask R-CNN,†Discret. Dyn. Nat. Soc., vol. 2020, 2020, doi: 10.1155/2020/9242917.

S. Ampamya, J. M. Kitayimbwa, and M. C. Were, “Performance of an open source facial recognition system for unique patient matching in a resource-limited setting,†Int. J. Med. Inform., vol. 141, no. May, p. 104180, 2020, doi: 10.1016/j.ijmedinf.2020.104180.

V. Sunanthini et al., “Comparison of CNN Algorithms for Feature Extraction on Fundus Images to Detect Glaucoma,†J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/7873300.

P. Peng, I. Portugal, P. Alencar, and D. Cowan, “A face recognition software framework based on principal component analysis,†PLoS One, vol. 16, no. 7, p. e0254965, Jul. 2021, doi: 10.1371/journal.pone.0254965.

S. Ben Chaabane, M. Hijji, R. Harrabi, and H. Seddik, “Face recognition based on statistical features and SVM classifier,†Multimed. Tools Appl., vol. 81, no. 6, pp. 8767–8784, 2022, doi: 10.1007/s11042-021-11816-w.

A. Alzu’bi, F. Albalas, T. Al-Hadhrami, L. B. Younis, and A. Bashayreh, “Masked face recognition using deep learning: A review,†Electron., vol. 10, no. 21, 2021, doi: 10.3390/electronics10212666.

H. Deng, Z. Feng, G. Qian, X. Lv, H. Li, and G. Li, “MFCosface: A Masked-Face Recognition Algorithm Based on Large Margin Cosine Loss,†Appl. Sci., vol. 11, no. 16, p. 7310, Aug. 2021, doi: 10.3390/app11167310.

N. Ud Din, K. Javed, S. Bae, and J. Yi, “A Novel GAN-Based Network for Unmasking of Masked Face,†IEEE Access, vol. 8, pp. 44276–44287, 2020, doi: 10.1109/ACCESS.2020.2977386.

N. S. Rafa et al., “IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic,†Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/6870358.

P. Dolibog, B. Pietrzyk, K. Kierszniok, and K. Pawlicki, “Comparative Analysis of Human Body Temperatures Measured with Noncontact and Contact Thermometers,†Healthc., vol. 10, no. 2, 2022, doi: 10.3390/healthcare10020331.

A. A. Rahimoon, M. N. Abdullah, and I. Taib, “Design of a contactless body temperature measurement system using Arduino,†Indones. J. Electr. Eng. Comput. Sci., vol. 19, no. 3, p. 1251, Sep. 2020, doi: 10.11591/ijeecs.v19.i3.pp1251-1258.

N. W.-J. Goh et al., “Design and Development of a Low Cost, Non-Contact Infrared Thermometer with Range Compensation,†Sensors, vol. 21, no. 11, p. 3817, May 2021, doi: 10.3390/s21113817.

W. Xia, J. Yan, and Y. Li, “STM32-Based Contactless Temperature Measurement and Identification Device,†IOP Conf. Ser. Earth Environ. Sci., vol. 692, no. 2, p. 022041, Mar. 2021, doi: 10.1088/1755-1315/692/2/022041.

Q. Xu, Z. Zhu, H. Ge, Z. Zhang, and X. Zang, “Effective Face Detector Based on YOLOv5 and Superresolution Reconstruction,†Comput. Math. Methods Med., vol. 2021, 2021, doi: 10.1155/2021/7748350.

K. H. Cheah, H. Nisar, V. V. Yap, C. Y. Lee, and G. R. Sinha, “Optimizing residual networks and vgg for classification of eeg signals: Identifying ideal channels for emotion recognition,†J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/5599615.

J. Tian, H. Xie, S. Hu, and J. Liu, “Multidimensional Face Representation in a Deep Convolutional Neural Network Reveals the Mechanism Underlying AI Racism,†Front. Comput. Neurosci., vol. 15, no. March, pp. 1–8, Mar. 2021, doi: 10.3389/fncom.2021.620281.

Z. Hu and Y. Wang, “Multiclass Interactive Martial Arts Teaching Optimization Method Based on Euclidean Distance,†Secur. Commun. Networks, vol. 2022, 2022, doi: 10.1155/2022/7272048.

L. Yu and X.-S. Gao, “Improve Robustness and Accuracy of Deep Neural Network with L2,∞ Normalization,†J. Syst. Sci. Complex., vol. 36, no. 1, pp. 3–28, 2023, doi: 10.1007/s11424-022-1326-y.

P. Huang, Q. Ye, F. Zhang, G. Yang, W. Zhu, and Z. Yang, “Double L2,p-norm based PCA for feature extraction,†Inf. Sci. (Ny)., vol. 573, pp. 345–359, Sep. 2021, doi: 10.1016/j.ins.2021.05.079.

S. R. Anan, M. A. Hossain, M. Z. Milky, M. M. Khan, M. Masud, and S. Aljahdali, “Research and Development of an IoT-Based Remote Asthma Patient Monitoring System,†J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/2192913.

K. Islam, F. Alam, A. I. Zahid, M. M. Khan, and M. Inamabbasi, “Internet of Things- (IoT-) Based Real-Time Vital Physiological Parameter Monitoring System for Remote Asthma Patients,†Wirel. Commun. Mob. Comput., vol. 2022, 2022, doi: 10.1155/2022/1191434.

M. Kasper-Eulaers, N. Hahn, S. Berger, T. Sebulonsen, Ø. Myrland, and P. E. Kummervold, “Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5,†Algorithms, vol. 14, no. 4, p. 114, Mar. 2021, doi: 10.3390/a14040114.

K. Yoon, J. Gwak, Y. M. Song, Y. C. Yoon, and M. G. Jeon, “OneShotDA: Online Multi-Object Tracker with One-Shot-Learning-Based Data Association,†IEEE Access, vol. 8, pp. 38060–38072, 2020, doi: 10.1109/ACCESS.2020.2975912.



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