Face recognition for smart door security access with convolutional neural network method

Dhimas Tribuana, Hazriani Hazriani, Abdul Latief Arda


This study focuses on enhancing office security through a smart door system, designed to protect sensitive documents and critical data. Emphasizing exclusive access for authorized personnel, the system integrates advanced biometric authentication, predominantly facial recognition. The project's aim is to optimize face recognition using convolutional neural network (CNN) techniques, identifying the best preprocessing methods and hyperparameter settings. A significant aspect of the research involves developing a smart door system with remote authentication and control capabilities via internet connectivity. Employing transfer learning with MobileNet V2, the study presents a compact model tailored for the Raspberry Pi platform. The model utilizes a dataset with five facial recognition classes and an additional class for unknown faces, ensuring a diverse representation. The trained model achieved a high accuracy (0.9729) and low loss (0.09). System evaluation revealed an overall accuracy of 0.96, perfect recall (1.00), and a precision of 0.897. These results demonstrate the system's efficacy in secure access control, making it a viable solution for contemporary office environments


classification; achine learning; deep learning; raspberry pi; thingsboard server;

Full Text:


DOI: http://doi.org/10.12928/telkomnika.v22i3.25946


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604