IoT-Based Locker Lock Security Using YOLO Facial Recognition

Authors

  • Legenda Prameswono Pratama Department of Electrical Engineering, Faculty of Engineering and Computer Science, Jakarta Global University, 16412, Indonesia
  • Sinka Wilyanti Department of Electrical Engineering, Faculty of Engineering and Computer Science, Jakarta Global University, 16412, Indonesia
  • Naufal Eka Putra Department of Electrical Engineering, Faculty of Engineering and Computer Science, Jakarta Global University, 16412, Indonesia

DOI:

https://doi.org/10.56904/j-gers.v5i1.201

Keywords:

Facial Recognition, YOLO, ESP32-CAM, Smart Locker, Internet of Things

Abstract

The increasing demand for secure and intelligent storage systems has encouraged the development of biometric-based authentication technologies. This study presents the design and implementation of a smart locker security system based on facial recognition using the YOLO (You Only Look Once) algorithm integrated with an ESP32-CAM module and IoT communication. The system was developed to provide automated user authentication and remote locker control through real-time face detection and recognition. Performance evaluation was conducted by analyzing face detection accuracy, authentication response time, and network communication quality under different distances and lighting conditions. Experimental results showed that the YOLO-based system achieved a a face detection success rate of up to 92% under both bright-light and low-light environments at distances of 30 cm and 100 cm. The detection error ranged from 2.60% to 4.30%, indicating stable and reliable detection performance. Authentication testing revealed that the system performed optimally at 30 cm, with response times ranging from 1.47 s to 2.56 s and error rates below 1.2%. At 100 cm, the response time increased to approximately 9–10 s, accompanied by higher error rates due to reduced facial feature visibility. In addition, network performance evaluation based on latency, packet loss, and throughput demonstrated reliable Wi-Fi communication between the ESP32-CAM and the server, ensuring smooth data transmission during authentication and locker control operations. The results confirm that the YOLO algorithm is effective for real-time facial recognition applications and can be successfully implemented in smart locker security systems. The proposed system provides accurate authentication, reliable communication, and enhanced security, making it suitable for practical access-control applications.

References

[1] A. Rahman et al., “Smart Keyless Locker Design Using Face Recognition Technology Based on the Internet of Things Jakarta Global University Classroom,” J. Glob. Eng. Res. Sci., vol. 4, no. 2, pp. 56–66, Dec. 2025, doi: 10.56904/j-gers.v4i2.162.

[2] A. Choudhary, “Internet of Things: a comprehensive overview, architectures, applications, simulation tools, challenges and future directions,” Discov. Internet Things, vol. 4, no. 1, p. 31, Dec. 2024, doi: 10.1007/s43926-024-00084-3.

[3] K. N. Sai, D. T. Sunil, and D. M. Eshwarappa, “A comprehensive review of door lock security systems,” Int. J. Circuit, Comput. Netw., vol. 5, no. 1, pp. 12–17, Jan. 2024, doi: 10.33545/27075923.2024.v5.i1a.61.

[4] S. Mohammed and A. H. Alkeelani, “Locker Security System Using Keypad and RFID,” in 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), IEEE, Jul. 2019, pp. 1–5. doi: 10.1109/ICCSRE.2019.8807588.

[5] A. Ramzan, W. Farhan, I. Malahat, and N. Afzal, “Double-Layered Authentication Door-Lock System Utilizing Hybrid RFID-PIN Technology for Enhanced Security,” in MTME 2025, Basel Switzerland: MDPI, Aug. 2025, p. 19. doi: 10.3390/materproc2025023019.

[6] R. Keote, S. S. Dandale, P. A. Bhagat, and M. Keote, “Biometric & GSM based Security System for Bank Lockers,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, Jun. 2024, pp. 1–5. doi: 10.1109/ICCCNT61001.2024.10725225.

[7] N. EL Fadel, “Facial Recognition Algorithms: A Systematic Literature Review,” J. Imaging, vol. 11, no. 2, p. 58, Feb. 2025, doi: 10.3390/jimaging11020058.

[8] M. L. Ali and Z. Zhang, “The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection,” Computers, vol. 13, no. 12, p. 336, Dec. 2024, doi: 10.3390/computers13120336.

[9] R. Sapkota et al., “YOLO advances to its genesis: a decadal and comprehensive review of the You Only Look Once (YOLO) series,” Artif. Intell. Rev., vol. 58, no. 9, p. 274, Jun. 2025, doi: 10.1007/s10462-025-11253-3.

[10] A. Vijayakumar and S. Vairavasundaram, “YOLO-based Object Detection Models: A Review and its Applications,” Multimed. Tools Appl., vol. 83, no. 35, pp. 83535–83574, Mar. 2024, doi: 10.1007/s11042-024-18872-y.

[11] A. A. Murat and M. S. Kiran, “A comprehensive review on YOLO versions for object detection,” Eng. Sci. Technol. an Int. J., vol. 70, p. 102161, Oct. 2025, doi: 10.1016/j.jestch.2025.102161.

[12] D. Díaz-Delgado, S. M. Vigil-Ramírez, L. J. Acho-Cachay, B. R. Tuanama-Chávez, and L. A. Rojas-Puerta, “IoT-Based Smart Lock with Real-Time Person Detection Using YOLOv5 and Mobile App Integration,” Rev. Científica Sist. e Informática, vol. 5, no. 2, p. e1005, Jul. 2025, doi: 10.51252/rcsi.v5i2.1005.

[13] G. C. Preethi, B. S. C. Sekhar, D. Venkatesh, S. Chanikya, K. Krishna, and T. Daniya, “Face recognition for smart door lock system using machine learning algorithms,” 2025, p. 020048. doi: 10.1063/5.0279225.

[14] R. Joshi, R. S. Somesula, and S. Katkoori, “Empowering Resource-Constrained IoT Edge Devices: A Hybrid Approach for Edge Data Analysis,” 2024, pp. 168–181. doi: 10.1007/978-3-031-45878-1_12.

[15] F. Ben Aicha, “Optimized embedded AI: efficient implementation of CNNs on ESP32-CAM for real-time image classification,” Computing, vol. 107, no. 11, p. 206, Nov. 2025, doi: 10.1007/s00607-025-01559-z.

[16] A. Maier, A. Sharp, and Y. Vagapov, “Comparative analysis and practical implementation of the ESP32 microcontroller module for the internet of things,” in 2017 Internet Technologies and Applications (ITA), IEEE, Sep. 2017, pp. 143–148. doi: 10.1109/ITECHA.2017.8101926.

[17] J. Zhang and N. Hu, “Accuracy and robustness evaluation of deep learning algorithms in facial recognition systems,” Syst. Soft Comput., vol. 7, p. 200252, Dec. 2025, doi: 10.1016/j.sasc.2025.200252.

[18] P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Comput. Sci., vol. 199, pp. 1066–1073, 2022, doi: 10.1016/j.procs.2022.01.135.

[19] T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: challenges, architectural successors, datasets and applications,” Multimed. Tools Appl., vol. 82, no. 6, pp. 9243–9275, Mar. 2023, doi: 10.1007/s11042-022-13644-y.

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Published

2026-06-30

How to Cite

Pratama, L. P., Wilyanti, S., & Eka Putra, N. (2026). IoT-Based Locker Lock Security Using YOLO Facial Recognition. Journal of Global Engineering Research and Science, 5(1), 37–44. https://doi.org/10.56904/j-gers.v5i1.201
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