Design Of Automatic Home Door Security Using Face Recognition with A Convolutional Neural Network (CNN) Model
DOI:
https://doi.org/10.55208/jeme.v3i2.02Keywords:
Face Recognition, ESP32, CNN, ArduinoAbstract
In Indonesia, the majority of residences continue to utilise traditional keys for door access. Keys are essential for house security; nonetheless, numerous criminals can circumvent this protection. A viable option to avert similar tragedies is to substitute the door security system with facial recognition technology. This security system employs CNN programming and an Arduino application. It utilises an ESP32 to autonomously open and close the door and engage a buzzer upon detecting a potential intruder. This research examines facial recognition technology utilised for door unlocking. The device exhibits a success rate of 71.4%, evaluated under various situations across five trials for each object.
Further upgrades to the gadget and system developed from this research can be achieved by incorporating more features and tools for detecting low light levels in a room. Furthermore, the system might be engineered to detect from considerable distances, such as many kilometres away.
References
Febriantono, M. A., Zuhair, A., & Khaeruddin. (2023). Smart Home Security System Using Face Recognition Based on IoT- CNN. 2023 International Conference on Information Technology Research and Innovation (ICITRI), 28–33. https://doi.org/10.1109/ICITRI59340.2023.10249929
Mishra, R., Ransingh, A., Behera, M. K., & Chakravarty, S. (2020). Convolutional Neural Network Based Smart Door Lock System. 2020 IEEE India Council International Subsections Conference (INDISCON), 151–156. https://doi.org/10.1109/INDISCON50162.2020.00041
Mun, H.-J., & Lee, M.-H. (2022). Design for Visitor Authentication Based on Face Recognition Technology Using CCTV. IEEE Access, 10, 124604–124618. https://doi.org/10.1109/ACCESS.2022.3223374
Nabila, L., Priharti, W., & Istiqomah. (2022). Design of Home Security System Using Face Recognition with Convolutional Neural Network Method. 2022 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 78–83. https://doi.org/10.1109/IAICT55358.2022.9887404
Phawinee, S., Cai, J.-F., Guo, Z.-Y., Zheng, H.-Z., & Chen, G.-C. (2021). Face recognition in an intelligent door lock with ResNet model based on deep learning. Journal of Intelligent & Fuzzy Systems, 40(4), 8021–8031. https://doi.org/10.3233/JIFS-189624
Radzi, S. A., Alif, M. K. M. F., Athirah, Y. N., Jaafar, A. S., Norihan, A. H., & Saleha, M. S. (2020). IoT based facial recognition door access control home security system using raspberry pi. International Journal of Power Electronics and Drive Systems (IJPEDS), 11(1), 417. https://doi.org/10.11591/ijpeds.v11.i1.pp417-424
Rahim, A., Zhong, Y., & Ahmad, T. (2022). A Deep Learning-Based Intelligent Face Recognition Method in the Internet of Home Things for Security Applications. Journal of Hunan University Natural Sciences, 49(10), 39–52. https://doi.org/10.55463/issn.1674-2974.49.10.6
Rahim, A., Zhong, Y., Ahmad, T., Ahmad, S., Pławiak, P., & Hammad, M. (2023). Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models. Sensors, 23(15), 6979. https://doi.org/10.3390/s23156979
Tribuana, D., Hazriani, H., & Arda, A. L. (2024). Face recognition for smart door security access with convolutional neural network method. TELKOMNIKA (Telecommunication Computing Electronics and Control), 22(3), 702. https://doi.org/10.12928/telkomnika.v22i3.25946
Downloads
Additional Files
Published
Issue
Section
License
Copyright (c) 2026 Mira Wati, Heri Wahyudi, Muhammad Syukri, Rizal Parghani (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
