Design and Implementation of a Face Recognition Door Lock System Using Raspberry Pi
Keywords:
Computer vision, Door lock system, Face recognition, LBPH algorithm, Raspberry Pi, Smart securityAbstract
Security is a major concern in residential, commercial, and institutional environments. Conventional door locking mechanisms such as mechanical keys, passwords, and RFID cards suffer from limitations including loss, duplication, and unauthorized access. To address these challenges, this paper presents the design and implementation of a face recognition-based smart door lock system using Raspberry Pi. The system utilizes computer vision techniques to identify authorized individuals and control a solenoid-based door lock. Facial images are captured using a camera module and processed using the local binary pattern histogram (LBPH) algorithm for face detection and recognition. Raspberry Pi serves as the central controller for image processing, decision-making, and hardware control. The system also provides user feedback through an LCD and optional voice assistance. Experimental results demonstrate reliable access control, enhanced security, and cost-effective implementation, making the system suitable for smart homes and secure premises. In addition, the proposed system emphasizes ease of use and scalability by allowing new users to be enrolled on the database with minimal effort. The use of open-source software libraries and affordable hardware components ensures low implementation cost while maintaining acceptable accuracy and performance. The face recognition approach eliminates the need for physical credentials, thereby reducing the risk of theft or misuse.
References
D. A. Sattar and B. V. Lakshmi, “Raspberry Pi face detection door lock,” J. Sci. Technol., vol. 7, no. 1, pp. 166–173, 2022.
D. A. Doshi, S. Suraj, R. Sagar, and A. Phopase, “IoT-based face recognition door lock system using Raspberry Pi,” Int. J. Sci. Res. Sci. Technol., vol. 8, no. 4, pp. 169–172, 2021.
O. E. Adetoyi and B. P. Awe, “Face recognition enabled door access control system,” FUOYE J. Eng. Technol., vol. 7, no. 1, 2025.
M. Alshar’e, S. S. F. A. Elnozahy, and S. C. Pari, “Raspberry Pi-based face recognition door lock system,” IoT, vol. 6, no. 2, p. 31, 2025.
B. Chandrasekaran, D. Karunkuzhali, V. Kandasamy, M. Dillibabu, and K. Rama Devi, “Real-time face detection and local binary patterns histogram-based face recognition on Raspberry Pi with OpenCV,” Int. J. Reconfigurable Embedded Syst., vol. 14, no. 2, p. 527, 2025.
S. R. Sandesh, A. Sridhar, R. T. P., S. Farheen, and S. Tameem, “Smart door lock/unlock using Raspberry Pi,” Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol., vol. 6, no. 3, p. 543, 2020.
A. D. Singh, B. S. Jangra, and R. Singh, “Face recognition door lock system using Raspberry Pi,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. 5, pp. 1733–1735, 2022.
S. S. Sandar and S. A. N. Oo, “Development of a secured door lock system based on face recognition using Raspberry Pi and GSM module,” Int. J. Trend Sci. Res. Dev., vol. 3, no. 5, pp. 357–361, 2019.
N. Tkauc, T. Tran, K. Hernandez-Diaz, and F. Alonso-Fernandez, “Cloud-based face and speech recognition for access control applications,” arXiv preprint, 2020.
V. Margapuri, N. Penumajji, and M. Neilsen, “PiBase: An IoT-based security system using Raspberry Pi and Google Firebase,” arXiv preprint, 2021.
K. C. Paul and S. Aslan, “An improved real-time face recognition system at low resolution based on local binary pattern histogram algorithm and CLAHE,” arXiv preprint, 2021.
A. A. A. Aboluhom and I. Kandilli, “Face recognition using deep learning on Raspberry Pi,” Comput. J., vol. 67, no. 10, 2024.
H. Lwin, A. S. Khaing, and H. M. Tun, “Automatic door access system using face recognition,” Int. J. Sci. Technol. Res., vol. 4, no. 6, 2015.
J. Ijaradar and J. Xu, “A cost-efficient real-time security surveillance system based on facial recognition using Raspberry Pi and OpenCV,” Curr. J. Appl. Sci. Technol., vol. 41, no. 5, 2022.
A. Sharmeela, B. Zafar, R. Kamal, and M. Aziz, “Biometric authentication technology: Facial recognition,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 10, no. 1, pp. 10–20, 2022.
M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86, 1991.
P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Kauai, HI, USA, 2001.
L. Zhang, M. Pawłowski, and K. Zimmermann, “Face recognition technology: A survey,” IEEE Access, vol. 8, pp. 141340–141370, 2020.
S. Z. Li and A. K. Jain, Handbook of Face Recognition, 2nd ed. New York, NY, USA: Springer, 2011.
A. K. Jain, A. Ross, and S. Pankanti, “Biometrics: A tool for information security,” IEEE Trans. Inf. Forensics Secur., vol. 1, no. 2, pp. 125–143, 2006.
Z. Ayop, W. M. H. Rosdi, et al., “Two-factor authentication smart entryway using modified LBPH algorithm,” arXiv preprint, Aug. 2025.
J. Daugman, “How iris recognition works,” IEEE Trans. Circuits Syst. Video Technol., vol. 14, no. 1, pp. 21–30, 2004.
R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: A survey,” Proc. IEEE, vol. 83, no. 5, pp. 705–741, 1995.
K. N. Plataniotis and A. N. Venetsanopoulos, Pattern Recognition. San Diego, CA, USA: Academic Press, 2000.
