Face recognition for smart door security access with
convolutional neural network method
Dublin Core
Title
Face recognition for smart door security access with
convolutional neural network method
convolutional neural network method
Subject
Classification
Machine learning
Deep learning
Raspberry Pi
Thingsboard server
Machine learning
Deep learning
Raspberry Pi
Thingsboard server
Description
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.
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.
Creator
Dhimas Tribuana, Hazriani, Abdul Latief Arda
Source
Journal homepage: http://telkomnika.uad.ac.id
Date
Feb 27, 2024
Contributor
PERI IRAWAN
Format
PDF
Language
ENGLISH
Type
TEXT
Files
Citation
Dhimas Tribuana, Hazriani, Abdul Latief Arda, “Face recognition for smart door security access with
convolutional neural network method,” Repository Horizon University Indonesia, accessed February 4, 2026, https://repository.horizon.ac.id/items/show/10147.
convolutional neural network method,” Repository Horizon University Indonesia, accessed February 4, 2026, https://repository.horizon.ac.id/items/show/10147.