Design and Build a Door Security System Using a Raspberry-Pi based Liveness Detection
DOI:
https://doi.org/10.33795/jartel.v14i4.5949Keywords:
Android, Face Recognition, Security, Firebase, Convolutional Neural NetworkAbstract
Various problems with the door threaten the security of the room owner such as theft. Many are done by thieves to carry out their actions. In the data from the National Police Center, there are 158,267 cases of theft with aggravation, the act of theft with aggravation is included in the theft at home or in a boarding room. This causes the need for a door security system. The purpose of this research is to make door security using Convolutional Neural Network (CNN) as accuracy detection and face prediction with Liveness Detection as real face detection or not. The overall test results of facial accuracy obtained based on epoch 25, epoch 50, epoch 100, epoch 150, epoch 200, and epoch 250. With average accuracy test results of 95.1% at epoch 25, 98.2% at epoch 50, 97.9% at epoch 100, 98.6% at epoch 150, 96.3% at epoch 200, and 95.7% at epoch 250. Prediction results were 95.3% at epoch 25, 98.1% at epoch 50, 97.7% at epoch 100, 98.6% at epoch 150, 96.3% at epoch 200, and 95.7% at epoch 250. Overall, the use of Liveness Detection and Convolutional Neural Network (CNN) technology in the door security system can improve security with more than 95% accuracy. This system also provides convenience for users to monitor the state of the door remotely through Android applications, so that it can be relied upon to improve room efficiency and security.
References
E. Fadly, S. A. Wibowo, and A. P. Sasmito, “Sistem keamanan pintu kamar kos menggunakan face recognition,” *JATI*, vol. 5, no. 2, 2021.
R. Kurniawan and A. Zulius, “Smart home security menggunakan face recognition berbasis Raspberry Pi,” *Jurnal Sustainable*, vol. 8, no. 2, 2019.
C. Lesmana, R. Lim, and L. W. Santoso, “Implementasi face recognition untuk akses ruangan pribadi,” *Jurnal Infra*, vol. 7, no. 1, 2019.
B. Wijayanto, F. Utaminingrum, and I. Arwani, “Face recognition untuk sistem pengaman rumah,” *Jurnal Pengembangan TI*, vol. 3, no. 3, 2019.
A. K. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” *IEEE Transactions on Circuits and Systems for Video Technology*, vol. 14, no. 1, 2004.
W. Zhao, R. Chellappa, A. Rosenfeld, and P. J. Phillips, “Face recognition: A literature survey,” *ACM Computing Surveys*, vol. 35, no. 4, 2003.
S. Z. Li and A. K. Jain, *Handbook of Face Recognition*. Springer, 2011.
R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: A survey,” *Proceedings of the IEEE*, vol. 83, no. 5, 1995.
A. Hadid, M. Pietikäinen, and T. Ahonen, “Face spoofing detection,” *IEEE Transactions on Information Forensics and Security*, vol. 7, no. 3, 2012.
I. Chingovska, A. Anjos, and S. Marcel, “On the effectiveness of local binary patterns in face anti-spoofing,” in *Proc. BIOSIG*, 2012.
Z. Boulkenafet, J. Komulainen, and A. Hadid, “Face anti-spoofing using color texture analysis,” *IEEE Transactions on Information Forensics and Security*, vol. 11, no. 8, 2016.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the gap to human-level performance in face verification,” in *Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)*, 2014.
F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” in *Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)*, 2015.
O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” in *Proc. British Machine Vision Conference (BMVC)*, 2015.
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng, “TensorFlow: A system for large-scale machine learning,” in *Proc. 12th USENIX Symp. on Operating Systems Design and Implementation (OSDI)*, 2016.
E. Upton and G. Halfacree, *Raspberry Pi User Guide*. Wiley, 2016.
I. Setiawan, A. Jaenul, and D. Priyokusumo, “Prototype sistem keamanan rumah menggunakan face recognition berbasis Raspberry Pi 4,” *Jurnal Poltekba*, vol. 4, no. 1, 2020.
R. Muwardi and R. R. Adisaputro, “Design sistem keamanan pintu menggunakan face detection,” *Jurnal Teknologi Elektro*, vol. 12, no. 3, 2021.
P. Jutika, “Implementasi face recognition berbasis Haar-cascade classifier pada sistem keamanan rumah,” *INFOTECH Journal*, 2022.
M. Susanto, F. E. Purnomo, and M. F. I. Fahmi, “Sistem keamanan pintu menggunakan metode Fisherface,” *Jurnal Ilmiah Inovasi*, vol. 17, no. 1, 2017.
