Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm
Dublin Core
Title
Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm
Subject
security system; convolutional neural network (CNN;, face recognition; biometric; VGG16
Description
Currently, security systems use conventional security methods, which provide low levels of security. Therefore, some
organizations now use biometric-based security systems, which include facial recognition-based systems. However, processing
facial data requires computationally intensive feature extraction, making real-time implementation difficult. Additionally, most
data used are from public datasets. In this study, we developed a facial recognition-based security system for door access using
a convolutional neural network (CNN) for real-time face recognition. We used the primary data of 102 students. The datasets
include two settings (i.e., outdoor and indoor) and three facial expressions (i.e., normal, smiley, and sleepy), amounting to
3060 samples. The training was performed using three deep-learning CNN architectures: Xception (model X), VGG16 (model
Y), and modified VGG16 (model Z). The best accuracy results of the three training architectures of model X, model Y, and
model Z for 100 epochs are 0.9469, 0.9971, and 1, respectively. In tests conducted on the 102 test data points, models X, Y,
and Z achieved accuracies of 50%, 97.05%, and 97.05%, respectively. These results indicate that the modified VGG16 (model
Z) is the best for real-time testing. In real-time tests conducted on the security system prototype with 15 respondents, the
resulting accuracy of model Z is 86.6%. This demonstrates that the modified VGG16 model has excellent recognition capabilities and can be implemented as a room access security system.
organizations now use biometric-based security systems, which include facial recognition-based systems. However, processing
facial data requires computationally intensive feature extraction, making real-time implementation difficult. Additionally, most
data used are from public datasets. In this study, we developed a facial recognition-based security system for door access using
a convolutional neural network (CNN) for real-time face recognition. We used the primary data of 102 students. The datasets
include two settings (i.e., outdoor and indoor) and three facial expressions (i.e., normal, smiley, and sleepy), amounting to
3060 samples. The training was performed using three deep-learning CNN architectures: Xception (model X), VGG16 (model
Y), and modified VGG16 (model Z). The best accuracy results of the three training architectures of model X, model Y, and
model Z for 100 epochs are 0.9469, 0.9971, and 1, respectively. In tests conducted on the 102 test data points, models X, Y,
and Z achieved accuracies of 50%, 97.05%, and 97.05%, respectively. These results indicate that the modified VGG16 (model
Z) is the best for real-time testing. In real-time tests conducted on the security system prototype with 15 respondents, the
resulting accuracy of model Z is 86.6%. This demonstrates that the modified VGG16 model has excellent recognition capabilities and can be implemented as a room access security system.
Creator
Immanuel Morries Pohan, Suci Dwijayanti, Bhakti Yudho Suprapto, Hera Hikmarika, Hermawati
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-0760
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Immanuel Morries Pohan, Suci Dwijayanti, Bhakti Yudho Suprapto, Hera Hikmarika, Hermawati, “Face Recognition-Based Room Access Security System Prototype using A Deep Learning Algorithm,” Repository Horizon University Indonesia, accessed April 21, 2026, https://repository.horizon.ac.id/items/show/10157.