Real-time Face Recognition System Using Deep Learning Method

  • Ayu Wirdiani Information Technology Department, Udayana University
  • I Ketut Gede Darma Putra Information Technology Department, Udayana University
  • Made Sudarma Electrical Engineering, Udayana University
  • Rukmi Sari Hartati Electrical Engineering, Udayana University
  • Lennia Savitri Azzahra Lofiana Information Technology Department, Udayana University

Abstract

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.


 

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References

[1] Darma Putra, Sistem Biometrika: Konsep Dasar, Teknik Analisis Citra, dan Tahapan Membengun Aplikasi Sistem Biometrika. 2009.
[2] S. H. Moi et al., "An Improved Approach to Iris Biometric Authentication Performance and Security with Cryptography and Error," International Journal on Informatics Visualization, vol. 6, no. August, pp. 531–539, 2022, doi: http://dx.doi.org/10.30630/joiv.6.2-2.1091.
[3] V. M. Arun Ross, Sudipta Banerjee, Cunjian Chen, Anurag Chowdhury, "Some Research Problems in Biometrics: The Future Beckons," Computer Vision and Pattern Recognition, 2 019.
[4] R. Blanco-Gonzalo et al., "Biometric Systems Interaction Assessment: The State of the Art," IEEE Transaction on Human-Machine Systems, vol. 49, no. 5, pp. 397–410, 2019, doi: 10.1109/THMS.2019.2913672.
[5] G. Rajkumar, V. Garg, A. Anand, and M. Eshasree, "Face Recognition Attendance Marking using YOLOv3," Internasional Journal of Advanced Science and Technology, vol. 29, no. 5, pp. 2806–2811, 2020.
[6] A. Malta, M. Mendes, and T. Farinha, "Augmented Reality Maintenance Assistant Using YOLOv5," Applied Sciences, vol. 11, pp. 1–14, 2021, doi: 10.3390/app11114758.
[7] K. B. Pranav and J. Manikandan, "Design and Evaluation of a Real-Time Face Recognition System using Convolutional Neural Networks," Procedia Computer Science, vol. 171, no. 2019, pp. 1651–1659, 2020, doi: 10.1016/j.procs.2020.04.177.
[8] I. G. P. S. Wijaya, A. Y. Husodo, and I. W. A. Arimbawa, “Real Time Face Recognition Based on Face Descriptor and Its Application,” Telkomnika, vol. 16, no. 2, pp. 739–746, 2018, doi: 10.12928/telkomnika.v16.i2.7418.
[9] P. Nagaraj, R. Banala, and A. V. K. Prasad, "Real Time Face Recognition using Effective Supervised Machine Learning Algorithms Real Time Face Recognition using Effective Supervised Machine Learning Algorithms," in Concilio 2021, 2021, pp. 1–7, doi: 10.1088/1742-6596/1998/1/012007.
[10] Y. Aufar and I. S. Sitanggang, "Face recognition based on Siamese convolutional neural network using Kivy framework," Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 2, pp. 764–772, 2022, doi: 10.11591/ijeecs.v26.i2.pp764-772.
[11] C. Song and S. Ji, "Face Recognition Method Based on Siamese Networks Under Non-Restricted Conditions," IEEE Access, vol. 10, no. 3, pp. 40432–40444, 2022, doi: 10.1109/ACCESS.2022.3167143.
[12] Y. Zhao and S. Geng, "Face Occlusion Detection Algorithm based on YoloV5," in 2nd Signal Processing and Computer Science, 2021, pp. 1–6, doi: 10.1088/1742-6596/2031/1/012053.
[13] F. H. Arby, I. Husni, and A. Amin, "Implementation of YOLO-v5 for a real-time Social Distancing Detection," Journal of Applied Informatics and Computing, vol. 6, no. 1, pp. 1–6, 2022.
[14] N. Hidayat, S. Wahyudi, and A. A. Diaz, "Individual Recognition Through Face Identification Based On You Only Look Once (YOLOv5) Method)," in Seminar Nasional Matematika, Geometri, Statistika, dan Komputasi SeNa-MaGeStiK 202, 2022, pp. 85–98.
[15] N. Dewi and F. Ismawan, “Implementasi Deep Learning Menggunakan Convolutional Neural Network untuk Sistem Pengenalan Wajah,” Faktor Exacta, vol. 14, no. 1, pp. 34–43, 2021, doi: 10.30998/faktorexacta.v14i1.8989.
[16] D. Setiawan, A. D. Putra, K. Stefani, and J. Felisa, "Implementasi Convolutional Neural Network untuk Facial Recognition," Media Informatika, vol. 20, no. 2, pp. 66–79, 2021.
[17] P. V. Lal, U. Srilakshmi, and D. Venkateswarlu, "Face Recognition Using Deep Learning Xception CNN Method," Journal of Theoretical and Applied Information Technology., vol. 100, no. 2, pp. 531–542, 2022.
[18] K. Sari and Y. Arvita, “Perancangan Sistem Absensi Facial Recognition menggunakan CNN dan Liveness Detector pada BPR Central Dana Mandiri,” Jurnal Informatika Dan Rekayasa Komputer, vol. 1, no. April, pp. 70–80, 2022.
[19] A. Singh, J. Kansari, and V. K. Sinha, "Face Recognition Using Transfer Learning by deep VGG16 model," Journal of Emerging Technologies Innovative Research, vol. 9, no. 4, pp. 121–127, 2022.
[20] G. Koch, R. Zemel, and R. Salakhudinov, "Siamese Neural Networks for One-shot Image Recognition," in International Conference on Machine Learning, p. 37, 2011.
[21] D. Li, Y. Yu, and X. Chen, "Object tracking framework with Siamese network and re-detection mechanism," EURASIP Journal on Wireless Communications and Networking, vol. 261, 2019, doi: https://doi.org/10.1186/s13638-019-1579-x.
[22] A. Nazareth, F. Radilla, K. Ruby, and P. Daniel, "Siamese Convolutional Neural Network for ASL Alphabet Recognition," Computacion y Sistemas, vol. 24, no. 3, pp. 1211–1218, 2020, doi: 10.13053/CyS-24-3-3481.
[23] A. Wirdiani, D. Putra, M. Sudarma, and R. S. Hartati, "Palmprint Identification using SVM and CNN Method," in 2021 International Conference on Smart-Green Technology in Electrical and Information Systems (ICSGTEIS), pp. 18–23, 2021,doi: 10.1109/ICSGTEIS53426.2021.9650406.
Published
2023-10-30
How to Cite
WIRDIANI, Ayu et al. Real-time Face Recognition System Using Deep Learning Method. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], v. 14, n. 1, p. 62-70, oct. 2023. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/107215>. Date accessed: 17 june 2024. doi: https://doi.org/10.24843/LKJITI.2023.v14.i01.p06.