Design A Multi Biometric System for Safe Access to Buildings
Keywords:
Multi Biometric, Convolution Neural Network, Thermal Imaging, Passive Security, COVID-19
Abstract
Designing a multi-biometric system for safe access to buildings is a very critical process because this system will act as an alternative of humans in observation, and must make smart decisions to protect the building from any intruder depending on the biometric information of the entering persons. In this paper a full design of a proposed multi-biometric system will be presented, two sensors are used (Visible and Thermal). the proposed system consists of several subsystems The first is the construction of a database that contains all the photos and information about each person who belongs to the building, the central system using visible and thermal cameras, and the peripheral system using visible cameras only. The central system is capable of remotely identifying and ensure of the health condition (Temperature, Heart Rate, Respiration Rate) of each entering person which is a very important process, especially with the COVID-19 pandemic. The peripheral system monitors any suspicious issue like crowed or walking outside allowed lines.
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References
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[1] J. S. Sahana, R. R. Tarun, and C. R. Manjunath, “Multi Biometric Recognition System,” Int. J. Comput. Sci. Mob. Comput., vol. 8, no. 6, pp. 89–94, 2019.
[2] W. Dahea and H. S. Fadewar, “Multimodal biometric system: A review,” Int. J. Res. Adv. Eng. Technol., vol. 4, no. 1, pp. 25–31, 2018.
[3] H. Purohit and P. K. Ajmera, “Optimal feature level fusion for secured human authentication in multimodal biometric system,” Mach. Vis. Appl., vol. 32, no. 1, pp. 1–12, 2021.
[4] S. Hamandi, A. M. Rahma, and R. Hassan, “Multi-Spectral Hybrid Invariant Moments Fusion Technique for Face Identification,” Int. Arab J. Inf. Technol., vol. 18, no. 3A, pp. 405–413, 2021, doi: https://doi.org/10.34028/iajit/18/3A/3.
[5] M. A. Sánchez-Sánchez et al., “Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems,” Entropy, vol. 22, no. 11, p. 1296, 2020.
[6] A. Jaiswal and S. Tarar, “Real-Time Biometric System for Security and Surveillance Using Face Recognition,” in International Conference on Advances in Computing and Data Sciences, 2020, pp. 293–304.
[7] M. CHOWDHURY, “A Robust Multimodal Biometric Scheme for Human Recognition and Authentication.” Charles Sturt University, 2020.
[8] Q. Memon, “Multi-Layered Multimodal Biometric Authentication for Smartphone Devices,” 2020.
[9] K. S. Bayram and B. Bolat, “Multibiometric identification by using ear, face, and thermal face,” EURASIP J. Image Video Process., vol. 2018, no. 1, pp. 1–8, 2018.
[10] P. Natarajan et al., “Multimodal feature fusion for robust event detection in web videos,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 1298–1305.
[11] D. T. Nguyen, H. G. Hong, K. W. Kim, and K. R. Park, “Person recognition system based on a combination of body images from visible light and thermal cameras,” Sensors, vol. 17, no. 3, p. 605, 2017.
[12] S. K. S. Modak and V. K. Jha, “Multibiometric fusion strategy and its applications: A review,” Inf. Fusion, vol. 49, pp. 174–204, 2019.
[13] L. M. Dinca and G. P. Hancke, “The fall of one, the rise of many: a survey on multi-biometric fusion methods,” IEEE Access, vol. 5, pp. 6247–6289, 2017.
[14] S. M. Hamandi, A. M. S. Rahma, and R. F. Hassan, “A New Hybrid Technique for Face Identification Based on Facial Parts Moments Descriptors,” Eng. Technol. J., vol. 39, no. 1B, pp. 117–128, 2021.
[15] S. Liu et al., “GIobalFusion: A Global Attentional Deep Learning Framework for Multisensor Information Fusion,” Proc. ACM Interactive, Mobile, Wearable Ubiquitous Technol., vol. 4, no. 1, pp. 1–27, 2020.
[16] A. Kadam, M. Ghadi, A. Chavan, P. Jawale, and B. Student, “Multimodal biometric fusion,” Int. J. Eng. Sci, vol. 12554, 2017.
[17] S. M. Hamandi, A. M. S. Rahma, and R. F. Hassan, “A New Hybrid Shape Moment Invariant Techniques for Face Identification in Thermal and Visible Visions,” in 2020 21st International Arab Conference on Information Technology (ACIT), 2020, pp. 1–9.
[18] J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks, vol. 61, pp. 85–117, 2015.
[19] C. Alippi, S. Disabato, and M. Roveri, “Moving convolutional neural networks to embedded systems: the alexnet and VGG-16 case,” in 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN), 2018, pp. 212–223.
[20] T. Greenhalgh, G. C. H. Koh, and J. Car, “Covid-19: a remote assessment in primary care,” Bmj, vol. 368, 2020.
[21] D. Dias and J. Paulo Silva Cunha, “Wearable health devices—vital sign monitoring, systems and technologies,” Sensors, vol. 18, no. 8, p. 2414, 2018.
[22] M. A. Said et al., “Novel vision-based thermal people counting tool for tracking infected people with viruses like covid-19,” J. Adv. Res. Dyn. Control Syst., vol. 12, no. 7 Special Issue, pp. 1115–1119, 2020, doi: 10.5373/JARDCS/V12SP7/20202210.
[23] A. Shajkofci, “Correction of human forehead temperature variations measured by non-contact infrared thermometer,” IEEE Sens. J., 2021.
[24] B. G. Vainer, “A novel high-resolution method for the respiration rate and breathing waveforms remote monitoring,” Ann. Biomed. Eng., vol. 46, no. 7, pp. 960–971, 2018.
[25] V. Pasquadibisceglie, G. Zaza, and G. Castellano, “A personal healthcare system for contact-less estimation of cardiovascular parameters,” in 2018 AEIT International Annual Conference, 2018, pp. 1–6.
[26] O. C. Kurban, T. Yildirim, and A. Bi̇lgi̇ç, “A multi-biometric recognition system based on deep features of face and gesture energy image,” in 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2017, pp. 361–364.
[27] A. H. Mohsin et al., “Real-time remote health monitoring systems using body sensor information and finger vein biometric verification: A multi-layer systematic review,” J. Med. Syst., vol. 42, no. 12, pp. 1–36, 2018.
[28] E. Jose, M. Greeshma, M. T. P. Haridas, and M. H. Supriya, “Face recognition based surveillance system using facenet and mtcnn on jetson tx2,” in 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 2019, pp. 608–613.
Published
2021-08-09
How to Cite
Abdul Monem S. Rahma, Rehab F. Hassan, & Hamandi, S. (2021). Design A Multi Biometric System for Safe Access to Buildings . Al-Qadisiyah Journal of Pure Science, 26(4), 275–287. https://doi.org/10.29350/qjps.2021.26.4.1341
Section
Special Issue (Silver Jubilee)
Copyright © Author(s) . This is an open access article distributed under the Creative Commons Attribution
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