Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi

Fadhillah Azmi

doi:10.55524/ijircst.2023.11.1.9

Abstract

In this study, a smart attendance system was created using computer vision techniques embedded in the Raspberry Pi device. The initial process is carried out by recording students taking certain courses and taking facial images for the needs of the system database. In the next stage, the system will be regulated according to the time of lecture entry to determine which students will attend the lecture. Every student who wants to enter the classroom is identified by taking facial images with a camera from the Raspberry Pi device to identify and determine the time students enter to attend lectures. Each image taken will be processed to detect the presence of a face using the Viola-Jones method and to extract features using the LBP method to obtain the feature value of each image. The results obtained will be stored in the system for the facial recognition process. The final stage of the system being built is to perform face recognition according to the initial image to carry out the attendance process. This process will be carried out using the normalized cross-correlation (NCC) technique, in which the highest feature similarity obtained between the initial image and the newly captured image is the result of recognition by the system. From the trials that have been carried out, the developed system gives good results in obtaining attendance management in a fairly efficient manner, and the algorithm proposed for facial recognition obtains good results with an accuracy rate of 97.54%.

Keywords

Smart Attendance System, Face Recognition, Raspberry Pi, LBP, Normalized Cross Correlation

References

[1] M. Kasiselvanathan, A. Kalaiselvi, S. P. Vimal, and V. Sangeetha, “Smart Attendance Management System Based On Face Recognition Algorithm Engineering, Communication,” Int. J. Pure Appl. Math., vol. 120, no. 5, pp. 1377–1384, 2018, doi: https://acadpubl.eu/hub/2018-120-5/5/531.pdf.

[2] K. R. Kiran and S. Mekala, “Face Recognition Attendance System using Raspberry Pi,” Int. J. Pure Appl. Math., vol. 118, no. 20, pp. 3061–3065, 2018, doi: https://acadpubl.eu/hub/2018-118-21/articles/21d/19.pdf.

[3] K. Alhanaee, M. Alhammadi, N. Almenhali, and M. Shatnawi, “Face recognition smart attendance system using deep transfer learning,” Procedia Comput. Sci., vol. 192, pp. 4093–4102, 2021, doi: 10.1016/j.procs.2021.09.184.

[4] G. Anitha, P. S. Devi, J. V. Sri, and D. Priyanka, “Face Recognition Based Attendance System Using Mtcnn and Facenet,” Zeichen, vol. 6, no. 8, pp. 189–195, 2020.

[5] A. S. Nadhan et al., “Smart Attendance Monitoring Technology for Industry 4.0,” J. Nanomater., vol. 2022, pp. 1–9, 2022, doi: 10.1155/2022/4899768.

[6] R. S. Sabeenian, S. Aravind, P. Arunkumar, P. Harrish Joshua, and G. Eswarraj, “Smart attendance system using face recognition,” J. Adv. Res. Dyn. Control Syst., vol. 12, no. 5 Special Issue, pp. 1079–1084, 2020, doi: 10.5373/JARDCS/V12SP5/20201860.

[7] T. Menezes, “Face Recognition Attendance System using Raspberry Pi,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 9, no. 8, pp. 1145–1149, 2021, doi: 10.22214/ijraset.2021.37499.

[8] A. K. L, I. V, C. A, R. PauL, and S. Banerjee, “Smart Attendance Management System Using Raspberry Pi and Deep Learning Technique,” 2022, doi: 10.4108/eai.7-12-2021.2315121.

[9] N. R and D. K. P, “Student Smart Attendance Through Face Recognition using Machine Learning Algorithm,” Int. J. Recent Technol. Eng., vol. 9, no. 1, pp. 2348–2352, 2020, doi: 10.35940/ijrte.a2927.059120.

[10] A. J. Moshayedi, A. S. Roy, L. liao, M. Gheisari, A. A. Abbasi, and S. M. H. Bamakan, “Automation Attendance Systems Approaches: A Practical Review,” BOHR Int. J. Internet Things Res., vol. 1, no. 1, pp. 7–15, 2021, doi: 10.54646/bijiotr.003.

[11] T. A. Dompeipen, M. E. I. Najoan, J. T. Elektro, U. Sam, and R. Manado, “SSD, Mobile-net,” vol. 16, no. 1, pp. 65–76, 2021.

[12] I. Gusti Ngurah Made Kris Raya, A. N. Jati, and R. E. Saputra, “Analysis realization of Viola-Jones method for face detection on CCTV camera based on embedded system,” Proc. 2017 Int. Conf. Robot. Biomimetics, Intell. Comput. Syst. Robionetics 2017, vol. 2017-Decem, pp. 1–5, 2017, doi: 10.1109/ROBIONETICS.2017.8203427.

[13] J. Efendi, M. I. Zul, and W. Yunanto, “Real time face recognition using eigenface and viola-jones face detector,” Int. J. Informatics Vis., vol. 1, no. 1, pp. 16–22, 2017, doi: 10.30630/joiv.1.1.15.

[14] S. Bakheet and A. Al-Hamadi, “Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification,” Comput. Biol. Med., vol. 137, no. August, p. 104781, 2021, doi: 10.1016/j.compbiomed.2021.104781.

[15] Priyanka and D. Kumar, “Feature Extraction and Selection of kidney Ultrasound Images Using GLCM and PCA,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 1722–1731, 2020, doi: 10.1016/j.procs.2020.03.382.

[16] Q. Zhang, “Facial expression recognition in VGG network based on LBP feature extraction,” Proc. - 2020 5th Int. Conf. Mech. Control Comput. Eng. ICMCCE 2020, pp. 2089–2092, 2020, doi: 10.1109/ICMCCE51767.2020.00454.

[17] S. J. Elias et al., “Face recognition attendance system using local binary pattern (LBP),” Bull. Electr. Eng. Informatics, vol. 8, no. 1, pp. 239–245, 2019, doi: 10.11591/eei.v8i1.1439.

[18] L. Zhang, B. Zhong, and A. Yang, “Building Change Detection using Object-Oriented LBP Feature Map in Very High Spatial Resolution Imagery,” 2019 10th Int. Work. Anal. Multitemporal Remote Sens. Images, MultiTemp 2019, pp. 1–4, 2019, doi: 10.1109/Multi-Temp.2019.8866919.

[19] A. Nakhmani and A. Tannenbaum, “A New Distance Measure Based on Generalized Image Normalized Cross-Correlation for Robust Video Tracking and Image Recognition,” Pattern Recognit Lett., 2012, doi: 10.1016/j.patrec.2012.10.025.A.

[20] N. T. Abdulsada and S. M. Ali, “Automatic Face Recognition using Normalized Cross Correlation (NCC) Function with Variable Template Size,” AIP Conf. Proc., vol. 2437, no. August, 2022, doi: 10.1063/5.0093157.

[21] A. Kaso, “Computation of the normalized cross-correlation by fast Fourier transform,” PLoS One, 2018, doi: 10.1371/journal.pone.0203434.

[22] A. Saleh, D. Suryandy, and J. Nainggolan, “Face Image Retrieval System Using Combination Method of Self Organizing Map and Normalized Cross Correlation,” J. Infokum, vol. 9, no. 2, pp. 219–228, 2021.

Cites this article as

[Fadhillah Azmi, Amir Saleh, Achmad Ridwan (2023), Smart Management Attendance System with Facial Recognition Using Computer Vision Techniques on the Raspberry Pi , International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-11, Issue-1, Page No-38-44], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Fadhillah Azmi

Department of Electrical Engineering, Universitas Medan Area, Medan, Indonesia

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