Design and Build of Masked Face Identification System and IoT-Based Body Temperature Measurement
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.
- There are currently no refbacks.
Published by INSIGHT - Indonesian Society for Knowledge and Human Development