Implementasi Sistem Gerbang Otomatis dengan Perpaduan Teknologi Pengenalan Pelat Nomor Kendaraan dan Pengenalan Wajah

Authors

  • Ihsan Ahmad Kamal Universitas Telkom Author
  • Fransiskus Abel Pramuadi Putra Universitas Telkom Author
  • Firas Maulana Lasidi Universitas Telkom Author
  • Wahmisari Priharti Universitas Telkom Author
  • Willy Anugrah Cahyadi Universitas Telkom Author

Keywords:

sistem kontrol, gerbang otomatis, pengenalan wajah, pengenalan pelat nomor, face_recognition, YOLOv8, PaddleOCR

Abstract

Automated access control systems have been widely used in recent years due to their high accuracy and security. In this study, we present an intelligent and secure electronic gate based on facial recognition and vehicle number plate recognition. The system combines multimodal biometric technology with face recognition using the 'face_recognition' package and automatic licence plate recognition (ALPR) using YOLOv8 and PaddleOCR to enhance the security of access to restricted areas. The system was able to correctly recognise 29 out of 30 faces, while all licence plates were accurately recognised, even at different times. The results show that the automated gate system has been successfully developed and tested with an accuracy rate of 96.67%. This success demonstrates the potential use of multimodal biometric technology to improve the security and efficiency of access control systems in various applications, such as office environments, residential areas and other public facilities. Further research can be directed towards improving the resilience of the system to different environmental conditions and expanding the database of recognised faces and vehicle number plates.

References

[1] S. Al-Maadeed, M. Bourif, A. Bouridane, R. Jiang. 2016. "Low-quality facial biometric verification via dictionary-based random pooling." Pattern Recognition, vol. 52, pp. 238-248.

[2] C. Tsai, W.C. Cheng, J.S. Taur, and C.W. Tao. 2006. "Face detection using eigenface and neural network." Proceedings of the 2006 IEEE International Conference on Systems, Man, and Cybernetics, pp. 4343-4347.

[3] A. Mahmood, M. Uzair, and S. Al-Maadeed. 2018. "Multi-order statistical descriptors for real-time face recognition and object classification." IEEE Access, vol. 6, pp. 12993-13004.

[4] S. S. Farfade, M. J. Saberian, and L. Li. 2015. "Multi-view face detection using deep convolutional neural networks." Proceedings of the 5th ACM International Conference on Multimedia Retrieval, pp. 643-650.

[5] X. Sun, P. Wu, and S. C. Hoi. 2018. "Face detection using deep learning: An improved faster RCNN approach." Neurocomputing, vol. 299, pp. 42-50.

[6] G. Liu et al. 2011. "The calculation method of road travel time based on license plate recognition technology." Advances in Information Technology and Education Communication in Computer and Information Science, vol. 201, pp. 385-389.

[7] C.-N. E. Anagnostopoulos et al. 2008. "License plate recognition from still images and video sequences: a survey." IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 377-391.

[8] L. T. A. Al-Mahbashi, N.A. Yusof, S. Shaharum, M. S. Karim, A. A. Faudzi. 2019. "Development of automated gate using automatic license plate recognition system." In *Proceedings of the 10th National Technical Seminar on Underwater System Technology*, pp. 459-466.

[9] R. Chen. 2019. "Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning." Image and Vision Computing, vol. 87, pp. 47-56.

[10] L. Connie, C. K. On, and A. Patricia. 2018. "A Review of Automatic License Plate Recognition System in Mobile-based Platform." Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 10, no. 3-2, pp. 77-82.

[11] R. N. Rodrigues, L. L. Ling, and V. Govindaraju. 2009. "Robustness of Multimodal Biometric Fusion Methods Against Spoof Attacks." Journal of Visual and Computing, doi:10.1016/j.jvlc.2009.01.010.

[12] A. Ross and A. K. Jain. 2004. "Multimodal Biometrics: An Overview." Proceedings of the 12th European Signal Processing Conference, pp. 1221–1224.

[13] Ross, Arun & Jain, Anil. (2004). "Multimodal biometrics: An overview." 1221-1224.

[14] “face-recognition · PyPI.” Accessed: Jul. 11, 2024. [Online]. Available: https://pypi.org/project/face-recognition/

[15] Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

[16] Du, Y., Li, C., Guo, R., Yin, X., Liu, W., Zhou, J., Bai, Y., Yu, Z., Yang, Y., Dang, Q. and Wang, H. (2020). Pp-ocr: A practical ultra lightweight ocr system. arXiv preprint arXiv:2009.09941.

[17] R. U. Projects, ‘License Plate Recognition Dataset’, Roboflow Universe. Roboflow, Dec-2022.

[18] B. L. R. Pambudi and W. S. Aji, “Serial Communication Module with Visual Basic and Arduino for Practical Use,” Buletin Ilmiah Sarjana Teknik Elektro, vol. 3, no. 2, pp. 130-136, 2021, doi: 10.12928/biste.v3i2.1494.

[19] “Face Recognition Dataset.” Accessed: Jul. 11, 2024. [Online]. Available: https://www.kaggle.com/datasets/vasukipatel/face-recognition-dataset

Downloads

Published

21/09/2024

How to Cite

[1]
“Implementasi Sistem Gerbang Otomatis dengan Perpaduan Teknologi Pengenalan Pelat Nomor Kendaraan dan Pengenalan Wajah”, jse, vol. 9, no. 4, Sep. 2024, Accessed: Dec. 13, 2024. [Online]. Available: https://jse.serambimekkah.id/index.php/jse/article/view/375

Most read articles by the same author(s)

Similar Articles

1-10 of 44

You may also start an advanced similarity search for this article.