DOI: https://doi.org/10.18517/ijods.5.1.56-62.2024
User-Friendly Interface Attendance System Based on Python Libraries and Deep Learning
Abstract
The old methods used to manage attendance for students, employees, or even teaching staff using a paper attendance record are considered tiring methods that take time. In addition to errors and repetitions or forgetting to register attendance, even with the presence of manual fingerprinting, due to the diseases that the world has experienced in previous years, it has become undesirable for some because it is considered a means of transmitting infection. In this research, we propose a method to record attendance relying on face recognition technology with real-time video processing by using multi-layer perceptron algorithm with two of python libraries, where the camera device is accessed, a picture of the person is taken, and the image is processed and framed, comparing captured faces with images within the stored database, performing face recognition, then dealing with file operations, and managing time-related tasks. Once the desired person is found, attendance is recorded with the actual time entered into an Excel file, and the file is saved with the date of the day on which attendance was recorded. The designed system works efficiently in the real-time implementation of counting and detection, proven to combine high face-detection accuracy and performance.
Article Details
How to Cite
[1]
A. K. Ali and J. Y. Mustafa, “User-Friendly Interface Attendance System Based on Python Libraries and Deep Learning”, Int. J. Data. Science., vol. 5, no. 1, pp. 56-62, Jun. 2024.
Issue
Section
Articles
References
Panchal K, Jingar P, Vataliya O, Rathod H.,” RFID Based Attendance System for Department”, Int J Intell Syst Appl Eng, 2024, 12(14s):505-14.
Kamil MHM, Zaini N, Mazalan L, Ahamad AHJMt., “Online attendance system based on facial recognition with face mask detection”, Multimedia Tools and Applications, 2023, 82(22):34437-57, doi:10.1007/s11042-023-14842-y.
Salunkhe S, Lad R, Kumar S, Mehta T, Bhegade R, “Integrated Student Database and Attendance Management System with Face Recognition” , EasyChair, 2023: 2516-2314.
González-Sabbagh SP, Robles-Kelly AJACS, “A survey on underwater computer vision”, 2023;55(13s):1-39,
doi: 10.1145/3578516.
Wanyonyi, D. and T.J.I.A. Celik, “Open-source face recognition frameworks: A review of the landscape”. IEEE Access, 2022. 10: p. 50601-50623, doi: 10.1109/ACCESS.2022.3170037.
Manjula DA, Kalpana D, Guguloth SJIJfIE, Research M, “Facial Recognition Attendance Monitoring System using Deep Learning Techniques”, International Journal for Innovative Engineering & Management Research, 2023, 12(3):129-137.
Petrescu RVJJoM, Robotics, “Face recognition as a biometric application”, Journal of Mechatronics and Robotics, 2019, 3:237.57, doi: 10.2139/ssrn.3417325.
Nosrati L, Bidgoli AM, Javadi HHSJIJoCIS, “Identifying People’s Faces in Smart Banking Systems Using Artificial Neural Networks”, Int J Comput Intell Syst, 2024, 17(1):1-21, doi: 10.1007/s44196-023-00383-7.
Leo M, Carcagnì P, Mazzeo PL, Spagnolo P, Cazzato D, Distante CJI, “Analysis of facial information for healthcare applications: A survey on computer vision-based approaches”, Information, 2020, 11(3):128,
doi: 10.3390/info11030128.
Ali M, Diwan A, Kumar DJIJoC, Systems D, “Attendance System Optimization through Deep Learning Face Recognition”, International Journal of Computing and Digital Systems, 2024, 15(1):1-12,
doi: 10.12785/ijcds/1501108.
Maharani DA, Machbub C, Rusmin PH, Yulianti L, editors, “Improving the capability of real-time face masked recognition using cosine distance”, 2020 6th International conference on interactive digital media (ICIDM), IEEE. 2020, doi: 10.1109/ICIDM51048.2020.9339677.
Razaq IS, Shukur BKJIJoIT, “Combining wavelet transforms features and high-level features using CNN for face morphing attack detection”, International Journal of Information Technology, 2023, 15(7):3957-66,
doi: 10.1007/s41870-023-01424-2.
H.L DG, KV, KBR, NKD, PMR, “Face Recognition based Attendance System”, International Journal of Engineering Research & Technology (IJERT), 2020;9(06):761-767.
Dev S, Patnaik T, editors, “Student attendance system using face recognition”, 2020 international conference on smart electronics and communication (ICOSEC), IEEE, 2020, doi: 10.1109/ICOSEC49089.2020.9215441.
Khan S, Akram A, Usman NJWPC, “Real time automatic attendance system for face recognition using face API and OpenCV”, Wireless Pers Commun, 2020, 113(1):469-480, doi: 10.1007/s11277-020-07224-2.
Preethi K, Vodithala S, editors, “Automated smart attendance system using face recognition”, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2021,
doi: 10.1109/ICICCS51141.2021.9432140.
Asaad, R.R., et al., “Image Processing with Python Libraries”, Academic Journal of Nawroz University, 2023, 12(2): p. 410-416.
Yose E, Victor V, Surantha N, “Portable smart attendance system on Jetson Nano”, Bulletin of Electr Eng & Inf (BEEI), 2024, 13(2):1050-1059, doi: 10.11591/eei. v13i2.6061.
Qashlim A, Basri B, Haeruddin H, Ardan A, Nurtanio I, Ilham A, “Smartphone Technology Applications for Milkfish Image Segmentation Using OpenCV Library”, Int. J. Interact. Mob. Technol, 2020, 14(08):150-163.
Kiselev, I. “Comparative analysis of libraries for computer vision OpenCV and AForge”, NET for use in gesture recognition system. in Journal of Physics: Conference Series. 2020. IOP Publishing.
Ismael KD, Irina SJIJoEE, Science C, “Face recognition using Viola-Jones depending on Python”, Indonesian Journal of Electrical Engineering and Computer Science, 2020, 20(3):1513-21, doi: 10.11591/ijeecs. v20.i3. pp1513-1521.
Mekala, V., et al., “Face recognition-based attendance system”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2019. 8(12): p. 520-525.
Boyapally SR, Supreethi KJIRJoMiET, Science, “Facial Recognition and Attendance System Using Dlib and Face Recognition Libraries”, International Research Journal of Modernization in Engineering Technology and Science, 2021, 3(1):409-17.
Turkoglu, B., E.J.E.S. Kaya, and a.I.J. Technology, “Training multi-layer perceptron with artificial algae algorithm”, Engineering Science and Technology, an International Journal (JESTECH), 2020. 23(6): p. 1342-1350, doi: 10.1016/j.jestch.2020.07.001
Desai M, Shah MJCe, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)”, Clinical eHealth, 2021, 4:1-11,
doi: 10.1016/j.ceh.2020.11.002.
Chen, Q., W. Zhang, and Y.J.I.A. Lou, “Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network”, IEEE Access, 2020, 8: p. 117365-117376, doi: 10.1109/ACCESS.2020.3004284.
Car Z, Šegota SB, Anđelić N, Lorencin I, Mrzljak VJC, medicine mmi, “Modeling the spread of COVID-19 infection using a multilayer perceptron, Computational and Mathematical Methods in Medicine”, 2020:1-10,
doi: 10.1155/2020/5714714.
Lorencin, I., et al., “Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis”, Artificial Intelligence in Medicine, 2020. 102: p. 101746, doi: 10.1016/j.artmed.2019.101746.
Shewale Y, Kumar S, Banait S, “Machine Learning Based Intrusion Detection in IoT Network Using MLP and LSTM”, Int J Intell Syst Appl Eng, 2023, 11(7s):210-23.
Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi AJA, “Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS”, Agriculture, 2021;11(5):408, doi: 10.3390/agriculture11050408.
Kamil MHM, Zaini N, Mazalan L, Ahamad AHJMt., “Online attendance system based on facial recognition with face mask detection”, Multimedia Tools and Applications, 2023, 82(22):34437-57, doi:10.1007/s11042-023-14842-y.
Salunkhe S, Lad R, Kumar S, Mehta T, Bhegade R, “Integrated Student Database and Attendance Management System with Face Recognition” , EasyChair, 2023: 2516-2314.
González-Sabbagh SP, Robles-Kelly AJACS, “A survey on underwater computer vision”, 2023;55(13s):1-39,
doi: 10.1145/3578516.
Wanyonyi, D. and T.J.I.A. Celik, “Open-source face recognition frameworks: A review of the landscape”. IEEE Access, 2022. 10: p. 50601-50623, doi: 10.1109/ACCESS.2022.3170037.
Manjula DA, Kalpana D, Guguloth SJIJfIE, Research M, “Facial Recognition Attendance Monitoring System using Deep Learning Techniques”, International Journal for Innovative Engineering & Management Research, 2023, 12(3):129-137.
Petrescu RVJJoM, Robotics, “Face recognition as a biometric application”, Journal of Mechatronics and Robotics, 2019, 3:237.57, doi: 10.2139/ssrn.3417325.
Nosrati L, Bidgoli AM, Javadi HHSJIJoCIS, “Identifying People’s Faces in Smart Banking Systems Using Artificial Neural Networks”, Int J Comput Intell Syst, 2024, 17(1):1-21, doi: 10.1007/s44196-023-00383-7.
Leo M, Carcagnì P, Mazzeo PL, Spagnolo P, Cazzato D, Distante CJI, “Analysis of facial information for healthcare applications: A survey on computer vision-based approaches”, Information, 2020, 11(3):128,
doi: 10.3390/info11030128.
Ali M, Diwan A, Kumar DJIJoC, Systems D, “Attendance System Optimization through Deep Learning Face Recognition”, International Journal of Computing and Digital Systems, 2024, 15(1):1-12,
doi: 10.12785/ijcds/1501108.
Maharani DA, Machbub C, Rusmin PH, Yulianti L, editors, “Improving the capability of real-time face masked recognition using cosine distance”, 2020 6th International conference on interactive digital media (ICIDM), IEEE. 2020, doi: 10.1109/ICIDM51048.2020.9339677.
Razaq IS, Shukur BKJIJoIT, “Combining wavelet transforms features and high-level features using CNN for face morphing attack detection”, International Journal of Information Technology, 2023, 15(7):3957-66,
doi: 10.1007/s41870-023-01424-2.
H.L DG, KV, KBR, NKD, PMR, “Face Recognition based Attendance System”, International Journal of Engineering Research & Technology (IJERT), 2020;9(06):761-767.
Dev S, Patnaik T, editors, “Student attendance system using face recognition”, 2020 international conference on smart electronics and communication (ICOSEC), IEEE, 2020, doi: 10.1109/ICOSEC49089.2020.9215441.
Khan S, Akram A, Usman NJWPC, “Real time automatic attendance system for face recognition using face API and OpenCV”, Wireless Pers Commun, 2020, 113(1):469-480, doi: 10.1007/s11277-020-07224-2.
Preethi K, Vodithala S, editors, “Automated smart attendance system using face recognition”, 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, 2021,
doi: 10.1109/ICICCS51141.2021.9432140.
Asaad, R.R., et al., “Image Processing with Python Libraries”, Academic Journal of Nawroz University, 2023, 12(2): p. 410-416.
Yose E, Victor V, Surantha N, “Portable smart attendance system on Jetson Nano”, Bulletin of Electr Eng & Inf (BEEI), 2024, 13(2):1050-1059, doi: 10.11591/eei. v13i2.6061.
Qashlim A, Basri B, Haeruddin H, Ardan A, Nurtanio I, Ilham A, “Smartphone Technology Applications for Milkfish Image Segmentation Using OpenCV Library”, Int. J. Interact. Mob. Technol, 2020, 14(08):150-163.
Kiselev, I. “Comparative analysis of libraries for computer vision OpenCV and AForge”, NET for use in gesture recognition system. in Journal of Physics: Conference Series. 2020. IOP Publishing.
Ismael KD, Irina SJIJoEE, Science C, “Face recognition using Viola-Jones depending on Python”, Indonesian Journal of Electrical Engineering and Computer Science, 2020, 20(3):1513-21, doi: 10.11591/ijeecs. v20.i3. pp1513-1521.
Mekala, V., et al., “Face recognition-based attendance system”, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2019. 8(12): p. 520-525.
Boyapally SR, Supreethi KJIRJoMiET, Science, “Facial Recognition and Attendance System Using Dlib and Face Recognition Libraries”, International Research Journal of Modernization in Engineering Technology and Science, 2021, 3(1):409-17.
Turkoglu, B., E.J.E.S. Kaya, and a.I.J. Technology, “Training multi-layer perceptron with artificial algae algorithm”, Engineering Science and Technology, an International Journal (JESTECH), 2020. 23(6): p. 1342-1350, doi: 10.1016/j.jestch.2020.07.001
Desai M, Shah MJCe, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN)”, Clinical eHealth, 2021, 4:1-11,
doi: 10.1016/j.ceh.2020.11.002.
Chen, Q., W. Zhang, and Y.J.I.A. Lou, “Forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network”, IEEE Access, 2020, 8: p. 117365-117376, doi: 10.1109/ACCESS.2020.3004284.
Car Z, Šegota SB, Anđelić N, Lorencin I, Mrzljak VJC, medicine mmi, “Modeling the spread of COVID-19 infection using a multilayer perceptron, Computational and Mathematical Methods in Medicine”, 2020:1-10,
doi: 10.1155/2020/5714714.
Lorencin, I., et al., “Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis”, Artificial Intelligence in Medicine, 2020. 102: p. 101746, doi: 10.1016/j.artmed.2019.101746.
Shewale Y, Kumar S, Banait S, “Machine Learning Based Intrusion Detection in IoT Network Using MLP and LSTM”, Int J Intell Syst Appl Eng, 2023, 11(7s):210-23.
Nosratabadi S, Ardabili S, Lakner Z, Mako C, Mosavi AJA, “Prediction of food production using machine learning algorithms of multilayer perceptron and ANFIS”, Agriculture, 2021;11(5):408, doi: 10.3390/agriculture11050408.