Optimizing Attendance System: Integrating Liveness Detection and Deep Learning for Reliable Face Recognition
DOI:
https://doi.org/10.30595/juita.v12i2.21738Keywords:
liveness detection, deep learning, face recognition, attendance management, integration technologyAbstract
The study focuses on using vitality detection and deep learning technologies in the context of facial recognition in an IT presence management project. The combination of deep learning with vitality detection provides a considerable advancement in security and effectiveness. This work integrated vitality-detecting technology with in-depth learning in facial recognition systems. Vitality detection technologies are used to verify the authenticity of persons by examining live indicators such as movements or facial expressions before face recognition. Meanwhile, deep learning is used to analyze and process facial photos correctly by learning from large amounts of data and recognizing facial features in depth. The study data set consists of 1300 photographs of professional school instructors taken with official authority. Model testing and training are carried out in the Google Colab environment, using Python and the Hardy package. The test findings showed an 87% accuracy in face recognition, proving the system's capacity to consistently identify persons and distinguish real from false ones. Furthermore, the performance of Liveness Detection achieves 92% accuracy, as does the integration of Live Detection technology with Deep Learning at 78%.
References
[1] I. D. Raji, T. Gebru, M. Mitchell, J. Buolamwini, J. Lee, and E. Denton, “Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing,” Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society (AIES), vol. 7, pp. 145–151, Feb. 2020, Doi: 10.1145/3375627.3375820.
[2] N. Seen Long, K. Amin Mohamad Sukri, and F. Sains Komputer dan Teknologi Maklumat, “Face Recognition Attendance System with Face Rolling Motion on Android,” Applied Information Technology and Computer Science, vol. 4, no. 2, pp. 266–282, 2023, Doi: 10.30880/aitcs.2023.04.02.016.
[3] Sawarkar, Damket, and Alane, “Attendance System by Face Recognition using Deep Learning,” International Research Journal of Modernization in Engineering Technology and Science, Feb. 2024, Doi: 10.56726/irjmets48994.
[4] A. Woubie, E. Solomon, and J. Attieh, “Maintaining Privacy in Face Recognition using Federated Learning Method,” IEEE Access, 2024, Doi: 10.1109/ACCESS.2024.3373691.
[5] M. R. Hussein, A. Farhan, and M. Rahman, “Computer Vision Based Automated Attendance System Using Face Recognition,” in 6th Industrial Engineering and Operations Management Bangladesh Conference, 2023. Doi: 10.46254/BA06.20230105.
[6] S. Sayyad, A. Mulla, N. Gote, P. Bhosale, P. Yadav, and I. Adsul, “Face Recognition for Classroom Attendance Based on Convolutional Neural Network,” International Journal of Intelligent Systems and Applications in Engineering IJISAE, vol. 12, no. 1, pp. 474–479, 2024, [Online]. Available: www.ijisae.org
[7] J. Viswanathan, K. E, N. S, and V. S, “Smart Attendance System using Face Recognition,” EAI Endorsed Transactions on Scalable Information Systems, Feb. 2024, Doi: 10.4108/EETSIS.5203.
[8] A. Golasangi, M. Choudri, P. Bulla, and V. Devaraddi, “A Survey on Face Recognition Based Attendance System,” International Journal of Research in Engineering, Science and Management, vol. 7, no. 2, pp. 15–18, Feb. 2024, Doi: 10.5281/ZENODO.10644334.
[9] P. Rastogi, P. Saravanan, G. H. Kerinab Beenu, I. Kaur, R. J. Anandhi, and S. Senthilkumar, “Analysis Face Recognition based Systems for Employees Attendance Machine Learning,” Proceedings of the 2023 2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023, pp. 515–520, 2023, Doi: 10.1109/ICAISS58487.2023.10250622.
[10] S. Achmad, A. Budiman, R. Aryatama Yaputera, and A. Kurniawan, “Student Attendance with Face Recognition (LBPH or CNN): Systematic Literature Review,” in 7th International Conference on Computer Science and Computational Intelligence 2022, 2022, pp. 31–38. Doi: 10.1016/j.procs.2022.12.108.
[11] T. V. Dang, “Smart Attendance System based on improved Facial Recognition,” Journal of Robotics and Control (JRC), vol. 4, no. 1, pp. 46–53, Feb. 2023, doi: 10.18196/JRC.V4I1.16808.
[12] R. Bartakke, S. Wahutre, R. Bhalerao, S. Chavan, and Y. Jaware, “Real Time Attendance System Using Image Processing,” International Research Journal of Modernization in Engineering, vol. 6, no. 1, pp. 2703–2709, 2024, [Online]. Available: www.irjmets.com
[13] E. P. Sochima, P. S. Ezekiel, O. E. Taylor, and F. B. Deedam-Okuchaba, “Smart Attendance Monitoring System Using Facial Recognition,” International Journal of Computer Techniques, vol. 8, no. 2, 2021, Accessed: Apr. 15, 2024. [Online]. Available: http://www.ijctjournal.org
[14] K. Yao, T. Kone, B. Gérard N’guessan, and K. F. Kouame, “Face Liveness Detection and Tracking in a Remote Exam Monitoring System,” Int J Innov Appl Stud, vol. 41, no. 2, pp. 588–597, 2023, Accessed: Apr. 15, 2024. [Online]. Available: http://www.ijias.issr-journals.org/
[15] S. Khairnar, S. Gite, K. Kotecha, and S. D. Thepade, “Face Liveness Detection Using Artificial Intelligence Techniques: A Systematic Literature Review and Future Directions,” Big Data and Cognitive Computing 2023, Vol. 7, Page 37, vol. 7, no. 1, p. 37, Feb. 2023, doi: 10.3390/BDCC7010037.
[16] I. A. Kamanga, J. M. Lyimo, I. A. Kamanga, and J. M. Lyimo, “Anti-spoofing detection based on eyeblink liveness testing for iris recognition,” International Journal of Science and Research Archive, vol. 7, no. 1, pp. 053–067, Sep. 2022, doi: 10.30574/IJSRA.2022.7.1.0186.
[17] S. Mandol, S. Mia, and S. M. M. Ahsan, “Real Time Liveness Detection and Face Recognition with OpenCV and Deep Learning,” 2021 5th International Conference on Electrical Information and Communication Technology, EICT 2021, 2021, doi: 10.1109/EICT54103.2021.9733685.
[18] I. A. Kamanga, J. M. Lyimo, I. A. Kamanga, and J. M. Lyimo, “Anti-spoofing detection based on eyeblink liveness testing for iris recognition,” International Journal of Science and Research Archive, vol. 7, no. 1, pp. 053–067, Sep. 2022, doi: 10.30574/IJSRA.2022.7.1.0186.
[19] O. Kuznetsov, D. Zakharov, E. Frontoni, A. Maranesi, and S. Bohucharskyi, “Cross-Database Liveness Detection: Insights from Comparative Biometric Analysis,” Jan. 2024, Accessed: Apr. 15, 2024. [Online]. Available: https://arxiv.org/abs/2401.16232v1
[20] M. Basurah, W. Swastika, and H. O. Kelana, “Implementation of Face Recognition and Liveness Detection Using TensorFlow.Js,” (JIP) Jurnal Informatika Polinema, vol. 9, no. 4, pp. 509–516, 2023, Accessed: Apr. 15, 2024. [Online]. Available: https://jurnal.polinema.ac.id/index.php/jip/article/view/3977/2759
[21] N. Surantha and B. Sugijakko, “Lightweight Face Recognition-based Portable Attendance System with Liveness Detection,” Internet of Things, vol. 25, p. 101089, Apr. 2024, doi: 10.1016/J.IOT.2024.101089.
[22] Y. Zhang, L. Zheng, V. L. L. Thing, R. Zimmermann, B. Guo, and Z. Yu, “FaceLivePlus: A Unified System for Face Liveness Detection and Face Verification,” ICMR 2023 - Proceedings of the 2023 ACM International Conference on Multimedia Retrieval, no. 23, pp. 144–152, Jun. 2023, doi: 10.1145/3591106.3592289.
[23] P. Matthew and S. Canning, “An algorithmic approach for optimizing biometric systems using liveness and coercion detection,” Comput Secur, vol. 94, p. 101831, Jul. 2020, doi: 10.1016/J.COSE.2020.101831.
[24] E. Lavens, D. Preuveneers, and W. Joosen, “Mitigating undesired interactions between liveness detection components in biometric authentication,” ACM International Conference Proceeding Series, Aug. 2023, doi: 10.1145/3600160.3604992.
[25] S. Das, I. De Ghosh, and A. Chattopadhyay, “A liveness detection system for sclera biometric applications,” Int J Biom, vol. 15, no. 6, pp. 645–664, 2023, doi: 10.1504/IJBM.2023.133956.
[26] O. Z. Jie, L. T. Ming, and T. C. Wee, “Biometric Authentication based on Liveness Detection Using Face Landmarks and Deep Learning Model,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 3–2, pp. 1057–1065, Nov. 2023, doi: 10.30630/JOIV.7.3-2.2330.
[27] B. Kaur, “Fingerprint and Iris liveness detection using invariant feature-set,” Multimed Tools Appl, pp. 1–27, Jan. 2024, doi: 10.1007/S11042-023-17854-W/METRICS.
[28] N. Shlezinger, J. Whang, Y. C. Eldar, and A. G. Dimakis, “Model-Based Deep Learning,” Proceedings of the IEEE, vol. 111, no. 5, pp. 465–499, May 2023, doi: 10.1109/JPROC.2023.3247480.
[29] D. Kothadiya et al., “Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach,” Journal of Imaging 2023, Vol. 9, Page 158, vol. 9, no. 8, p. 158, Aug. 2023, doi: 10.3390/JIMAGING9080158.
[30] S. Sujanthi, A. Bowshika, S. K. Dharaneesh, and A. Jai Sivadharsini, “Iris Liveness Detection using Deep Learning Networks,” 7th International Conference on Trends in Electronics and Informatics, ICOEI 2023 - Proceedings, pp. 1188–1196, 2023, doi: 10.1109/ICOEI56765.2023.10125665.
[31] B. Karatay, D. Beştepe, K. Sailunaz, T. Özyer, and R. Alhajj, “CNN-Transformer based emotion classification from facial expressions and body gestures,” Multimed Tools Appl, vol. 83, no. 8, pp. 23129–23171, Mar. 2024, doi: 10.1007/S11042-023-16342-5/METRICS.
[32] D. H. Lee and J. H. Yoo, “CNN Learning Strategy for Recognizing Facial Expressions,” IEEE Access, vol. 11, pp. 70865–70872, 2023, doi: 10.1109/ACCESS.2023.3294099.
[33] C. Ukamaka Betrand, C. Juliet Onyema, M. Benson-Emenike, D. Allswell Kelechi, and M. Eberechi Benson-Emenike, “Authentication System Using Biometric Data for Face Recognition,” International Journal of Sustainable Development Research, vol. 9, no. 4, pp. 68–78, 2023, doi: 10.11648/j.ijsdr.20230904.12.
[34] P. Babu Jha et al., “An Automated Attendance System Using Facial Detection and Recognition Technology,” Apex Journal of Business and Management, vol. 01, no. 01, pp. 103–120, 2023, doi 10.61274/apxc.2023.v01i01.008.
[35] H. O. Ikromovich and B. B. Mamatkulovich, “Facial Recognition Using Transfer Learning in the Deep CNN,” Open Access Repository, vol. 4, no. 3, pp. 502–507, Mar. 2023, doi: 10.17605/OSF.IO/NRMK2.
[36] Z. Chen, J. Chen, G. Ding, and H. Huang, “A lightweight CNN-based algorithm and implementation on embedded system for real-time face recognition,” Multimed Syst, vol. 29, no. 1, pp. 129–138, Feb. 2023, doi: 10.1007/S00530-022-00973-Z/METRICS.
[37] A. Rahim, Y. Zhong, T. Ahmad, S. Ahmad, P. Pławiak, and M. Hammad, “Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models,” Sensors 2023, Vol. 23, Page 6979, vol. 23, no. 15, p. 6979, Aug. 2023, doi: 10.3390/S23156979.
[38] J. Mohammed Sahan, E. I. Abbas, and Z. M. Abood, “A facial recognition using a combination of a novel one dimension deep CNN and LDA,” Mater Today Proc, vol. 80, pp. 3594–3599, Jan. 2023, doi: 10.1016/J.MATPR.2021.07.325.
[39] K. Painuly, Y. Bisht, H. Vaidya, A. Kapruwan, and R. Gupta, “Efficient Real-Time Face Recognition-Based Attendance System with Deep Learning Algorithms,” Proceedings of the 2nd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024, 2024, doi: 10.1109/IITCEE59897.2024.10467743.
[40] J. RajaSekhar, S. Sakhamuri, A. Dhruva Teja, and T. Siva Sai Bhargav, “Automatic Attendance Management System Using AI and Deep Convolutional Neural Network,” Advanced Technologies and Societal Change, pp. 67–76, 2023, doi: 10.1007/978-981-19-4522-9_7.
[41] C. Bisogni, L. Cimmino, M. De Marsico, F. Hao, and F. Narducci, “Emotion recognition at a distance: The robustness of machine learning based on hand-crafted facial features vs deep learning models,” Image Vis Comput, vol. 136, p. 104724, Aug. 2023, doi: 10.1016/J.IMAVIS.2023.104724.
[42] G. Meena, K. K. Mohbey, A. Indian, M. Z. Khan, and S. Kumar, “Identifying emotions from facial expressions using a deep convolutional neural network-based approach,” Multimed Tools Appl, vol. 83, no. 6, pp. 15711–15732, Feb. 2024, doi: 10.1007/S11042-023-16174-3/METRICS.
[43] M. Karnati, A. Seal, D. Bhattacharjee, A. Yazidi, and O. Krejcar, “Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey,” IEEE Trans Instrum Meas, vol. 72, 2023, doi: 10.1109/TIM.2023.3243661.
[44] D. Mamieva, A. B. Abdusalomov, M. Mukhiddinov, and T. K. Whangbo, “Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach,” Sensors 2023, Vol. 23, Page 502, vol. 23, no. 1, p. 502, Jan. 2023, doi: 10.3390/S23010502.
[45] K. Sarvakar, R. Senkamalavalli, S. Raghavendra, J. Santosh Kumar, R. Manjunath, and S. Jaiswal, “Facial Emotion Recognition using Convolutional Neural Networks,” Mater Today Proc, vol. 80, pp. 3560–3564, Jan. 2023, doi: 10.1016/J.MATPR.2021.07.297.
[46] B. Abd El-Rahiem et al., “An Efficient Deep Learning Model for Classification of Thermal Face Images,” Journal of Enterprise Information Management, vol. 36, no. 3, pp. 706–717, Apr. 2023, doi: 10.1108/JEIM-07-2019-0201/FULL/XML.
[47] Ragedhaksha, Darshini, Shahil, and J. Arunnehru, “Deep learning-based real-world object detection and improved anomaly detection for surveillance videos,” Mater Today Proc, vol. 80, pp. 2911–2916, Jan. 2023, doi: 10.1016/J.MATPR.2021.07.064.
[48] R. Sanders, “The Pareto Principle: Its Use and Abuse,” Journal of Services Marketing, vol. 1, no. 2, pp. 37–40, 1987, doi: 10.1108/EB024706/FULL/XML.
[49] X. Wang, K. Luo, and W. C. Lau, “Living a Lie: Security Analysis of Facial Liveness Detection Systems in Mobile Apps,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14585 LNCS, pp. 432–459, 2024, doi: 10.1007/978-3-031-54776-8_17/FIGURES/9.
[50] A. S. Sanchez-Moreno, J. Olivares-Mercado, A. Hernandez-Suarez, K. Toscano-Medina, G. Sanchez-Perez, and G. Benitez-Garcia, “Efficient face recognition system for operating in unconstrained environments,” J Imaging, vol. 7, no. 9, Sep. 2021, doi: 10.3390/jimaging7090161.
[51] S. Anwarul and S. Dahiya, “A Comprehensive Review on Face Recognition Methods and Factors Affecting Facial Recognition Accuracy,” Lecture Notes in Electrical Engineering, vol. 597, pp. 495–514, 2020, doi: 10.1007/978-3-030-29407-6_36.
[52] J. G. Cavazos, P. J. Phillips, C. D. Castillo, and A. J. O’Toole, “Accuracy Comparison across Face Recognition Algorithms: Where Are We on Measuring Race Bias?,” IEEE Trans Biom Behav Identity Sci, vol. 3, no. 1, pp. 101–111, Jan. 2021, doi: 10.1109/TBIOM.2020.3027269.
[53] M. Zhou, Q. Wang, Q. Li, W. Zhou, J. Yang, and C. Shen, “Securing Face Liveness Detection on Mobile Devices Using Unforgeable Lip Motion Patterns,” IEEE Trans Mob Comput, no. 01, pp. 1–16, Feb. 2024, doi: 10.1109/TMC.2024.3367781.
Downloads
Published
How to Cite
Issue
Section
License

JUITA: Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International License.








