Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network
DOI:
https://doi.org/10.30812/matrik.v22i1.2350Keywords:
Automatic door access, Amason face recognition, Convolutional Neural Network, Facial recognition, RaspberryAbstract
Automatic door access technology by utilizing biometrics such as fingerprints, retinas and facial structures is constantly evolving. The use of masks during the Covid-19 Pandemic and post-pandemic has become an obligation wherever humans are active. The study aimed to create an automated door access model using Convolutional Neural Network (CNN) algorithms and Amazon Rekognition as cloud-based software. The CNN algorithm is applied to classify faces wearing masks or not wearing masks. The CNN architecture model uses sequential, convolution2D, max polling 2D, flatten dan dense. The hardware includes the Raspberry Pi, USB Webcam, Relay, and Magnetic Doorlock. The test results were obtained from the results of the accuracy plot on the Convolutional Neural Network model with an accuracy rate of 99% at an epoch value of 8 with a learning time of 67 seconds.
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References
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[4] A. Apriani, H. Zakiyudin, and K. Marzuki, “Penerapan Algoritma Cosine Similarity dan Pembobotan TF-IDF System Penerimaan Mahasiswa Baru pada Kampus Swasta,†Jurnal Bumigora Information Technology (BITe), vol. 3, no. 1, pp. 19–27, 2021, doi: 10.30812/bite.v3i1.1110.
[5] I. F. Ashari, M. D. Satria, and M. Idris, “Parking System Optimization Based on IoT using Face and Vehicle Plat Recognition via Amazon Web Service and ESP-32 CAM,†Computer Engineering and Applications Journal, vol. 11, no. 2, pp. 137–153, 2022, doi: 10.18495/comengapp.v11i2.409.
[6] M. F. Aslan, K. Sabanci, A. Durdu, and M. F. Unlersen, “COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization,†Computers in Biology and Medicine, 2020.
[7] M. N. Baay, A. N. Irfansyah, and M. Attamimi, “Sistem Otomatis Pendeteksi Wajah Bermasker Menggunakan Deep Learning,†Jurnal Teknik ITS, vol. 10, no. 1, pp. 64–70, Aug. 2021, doi: 10.12962/j23373539.v10i1.59790.
[8] A. H. Bachtiar, P. P. Surya, and R. P. Astutik, “Rancang Bangun Dual Keamanan Sistem Pintu Rumah Menggunakan Pengenalan Wajah dan Sidik Jari Berbasis Iot (Internet of Things),†Jurnal POLEKTRO: Jurnal Power Elektronik, vol. 1, no. 1, pp. 102–107, 2022.
[9] A. D, “Face Recognition using Machine Learning Algorithms,†JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES, vol. 14, no. 3, Jun. 2019, doi: 10.26782/jmcms.2019.06.00017.
[10] P. Elechi, E. Okowa, and U. Ekwueme, “Facial Recognition Based Smart Door Lock System,†FUPRE Journal of Scientific and Industrial Research, vol. 6, no. 2, pp. 95–105, 2022.
[11] A. Febriansyah, J. Saputra, and P. Desvirati, “Alat Pendeteksi Suhu Tubuh dan Wajah (Kebutuhan Bukti Kehadiran) Berbasis Data,†Manutech : Jurnal Teknologi Manufaktur, vol. 14, no. 01, pp. 1–6, 2022.
[12] Gaurav Dhiman, Srihari. K, Ramesh. R, and Udayakumar. E, “An Innovative Approach for Face Recognition Using Raspberry Pi,†Artificial Intelligence Evolution, vol. 10, no. 1, pp. 102–107, Aug. 2020, doi: 10.37256/aie.12202062.
[13] H. Hairani, A. S. Suweleh, and D. Susilowaty, “Penanganan Ketidak Seimbangan Kelas Menggunakan Pendekatan Level Data,†MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 1, pp. 109–116, 2020, doi: 10.30812/matrik.v20i1.846.
[14] A. Hassouneh, A. M. Mutawa, and M. Murugappan, “Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods,†Informatics in Medicine Unlocked, vol. 20, no. 1, pp. 2–9, 2020, doi: 10.1016/j.imu.2020.100372.
[15] S. E. Oltean, “Mobile Robot Platform with Arduino Uno and Raspberry Pi for Autonomous Navigation,†Procedia Manufacturing, vol. 32, pp. 572–577, 2019, doi: 10.1016/j.promfg.2019.02.254.
[16] R. R. Ramdhani, R. I. Adam, and A. A. Ridha, “Implementasi Deep Learning Untuk Deteksi Masker Deep Learning Implementation for Face Mask Detection,†Journal of Information Technology and Computer Science (INTECOMS), vol. 4, no. 2, p. 2021, 2021.
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Published
2022-11-30
Issue
Section
Articles
How to Cite
[1]
T. A. Zuraiyah, S. Maryana, and A. Kohar, “Automatic Door Access Model Based on Face Recognition using Convolutional Neural Network”, MATRIK, vol. 22, no. 1, pp. 241–258, Nov. 2022, doi: 10.30812/matrik.v22i1.2350.
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