Preventive Attendance Record using Photo from Mobile Phone and Printed Paper using CNN
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Abstract
Face-based attendance systems are increasingly popular for their ease of use, but they are susceptible to fraud, such as using photos or videos for unauthorized attendance. This study introduces a digital attendance system that combines facial recognition with liveness detection powered by Convolutional Neural Networks (CNN). Liveness verification is achieved by analyzing subtle movements and responses to ambient lighting. The dataset includes 30 facial images, encompassing both authentic and fraudulent samples. Testing demonstrates a facial recognition accuracy of 91.3% and effective spoofing detection in static and dynamic settings. This system provides a secure, fraud-resistant attendance solution ideal for educational and corporate settings. Further enhancements are suggested to improve performance across diverse facial expressions and lighting conditions.
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References
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[2] D. Menotti et al., “Deep Representations for Iris, Face, and Fingerprint Spoofing Detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 864–879, Apr. 2015, doi: 10.1109/TIFS.2015.2398817.
[3] Y. Wei, I. K. D. Machica, C. E. Dumdumaya, J. C. T. Arroyo, and A. P. Delima, “Liveness Detection Based on Improved Convolutional Neural Network for Face Recognition Security,” International Journal of Emerging Technology and Advanced Engineering, vol. 12, no. 8, pp. 45–53, Aug. 2022, doi: 10.46338/ijetae0822_06.
[4] A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Future Generation Computer Systems, vol. 99, pp. 213–225, Oct. 2019, doi: 10.1016/j.future.2019.04.025.
[5] A. Celecia, K. Figueiredo, M. Vellasco, and R. González, “A Portable Fuzzy Driver Drowsiness Estimation System,” Sensors, vol. 20, no. 15, p. 4093, Jul. 2020, doi: 10.3390/s20154093.
[6] R. Mahum et al., “A robust framework to generate surveillance video summaries using combination of zernike moments and r-transform and deep neural network,” Multimed Tools Appl, vol. 82, no. 9, pp. 13811–13835, Apr. 2023, doi: 10.1007/s11042-022-13773-4.
[7] S. S. B. Benslet, P. Parameswari, and S. S. B. Benslet, “Criminal Facial Recognition Based on Multi Stage Progressive V-Net and MTCNN with NASnet Architecture,” 2025. [Online]. Available: https://www.jneonatalsurg.com
[8] K. Maisha and S. N. Shetu, “Influencing factors of e-learning adoption amongst students in a developing country: the post-pandemic scenario in Bangladesh,” Future Business Journal, vol. 9, no. 1, p. 37, Aug. 2023, doi: 10.1186/s43093-023-00214-3.
[9] A. Pandit, R. Nikalje, N. Vishwakarma, V. Vishwasrao, and Prof. T. Khose, “Face Authentication using MTCNN and FaceNet,” Int J Res Appl Sci Eng Technol, vol. 11, no. 11, pp. 1140–1143, Nov. 2023, doi: 10.22214/ijraset.2023.56679.
[10] J. A. Starosta and B. Izydorczyk, “Understanding the Phenomenon of Binge-Watching—A Systematic Review,” Int J Environ Res Public Health, vol. 17, no. 12, p. 4469, Jun. 2020, doi: 10.3390/ijerph17124469.
[11] C. Qin, X. Lu, P. Zhang, H. Xie, and W. Zeng, “Identity Recognition Based on Face Image,” J Phys Conf Ser, vol. 1302, no. 3, p. 032049, Aug. 2019, doi: 10.1088/1742-6596/1302/3/032049.
[12] F. N. Azmi, T. A. Z, and M. I. Suriansyah, “Sistem Keamanan Penerimaan Paket Menggunakan Face Recognition dan Metode Comparison Algorithms dengan Rasberry Pi Berbasis Internet of Things,” J-INTECH, vol. 12, no. 1, pp. 1–13, Jun. 2024, doi: 10.32664/j-intech.v12i1.1162.
[13] Y. Yanuardi, L. Azhari, A. A. J. Sinlae, and A. D. Alexander, “Pengembangan Sistem Pengaduan Layanan Masyarakat Menggunakan Metode Rapid Application Development (RAD),” J-INTECH, vol. 12, no. 1, pp. 36–48, Jun. 2024, doi: 10.32664/j-intech.v12i1.1201.
[14] R. E. Saragih and Q. H. To, “A Survey of Face Recognition Based on Convolutional Neural Network,” 2022.
[15] Z. Sun and Z. Liu, “Ensuring privacy in face recognition: a survey on data generation, inference and storage,” Discover Applied Sciences, vol. 7, no. 5, p. 441, May 2025, doi: 10.1007/s42452-025-06987-2.
[16] R. Jannah, M. Walid, and H. Hoiriyah, “Sistem Pengenalan Citra Dokumen Tanda Tangan Menggunakan Metode CNN (Convolutional Neural Network),” Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik, vol. 12, no. 2, pp. 1–8, Dec. 2022, doi: 10.51747/energy.v12i2.1116.
[17] F. F. Wati, A. E. Widodo, and D. Abror, “Sistem Informasi Penerimaan Karyawan Berbasis Website Pada PT Federal International Finance Cabang Kota Tegal,” J-INTECH, vol. 12, no. 1, pp. 62–72, Jun. 2024, doi: 10.32664/j-intech.v12i1.1189.
[18] I. Ramadhan, N. Nugroho, H. Kurniawanto, and J. Warta, “Sistem Pendukung Keputusan Menggunakan Metode WASPAS Untuk Pemilihan Aplikasi Manajemen Bisnis dan Keuangan,” J-INTECH, vol. 12, no. 1, pp. 49–61, Jun. 2024, doi: 10.32664/j-intech.v12i1.1214.
[19] S. Khan, M. H. Javed, E. Ahmed, S. A. A. Shah, and S. U. Ali, “Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses,” in 2019 International Conference on Information Science and Communication Technology (ICISCT), IEEE, Mar. 2019, pp. 1–6. doi: 10.1109/CISCT.2019.8777442.
[20] V. Musanga, C. Chibaya, and S. Viriri, “A scoping review of literature on deep learning and symbolic AI-based framework for detecting Covid-19 using computerized tomography scans,” International Journal of Research in Business and Social Science (2147- 4478), vol. 13, no. 2, pp. 412–419, Apr. 2024, doi: 10.20525/ijrbs.v13i2.2955.
[21] A. Ahmed, J. Guo, F. Ali, F. Deeba, and A. Ahmed, “LBPH based improved face recognition at low resolution,” in 2018 International Conference on Artificial Intelligence and Big Data, ICAIBD 2018, Institute of Electrical and Electronics Engineers Inc., Jun. 2018, pp. 144–147. doi: 10.1109/ICAIBD.2018.8396183.
[22] P. Archana, N. Reddy, M. Bhavya, Y. Sharan, and M. Shashank, “Recognizing Very Small Face Images Using Convolution Neural Networks,” 2023.