Deteksi Deteksi Wajah Bermasker Berbasis Tensorflow-Keras untuk Pengendalian Gerbang Akses Masuk Menggunakan Raspberry Pi 4

Tensorflow-Keras Base Masked Face Detection For Access Control Gate Using Raspberry Pi 4 in Align With COVID-19 Health Protocol

  • Friendly Friendly Politeknik Negeri Medan
  • Zakaria Sembiring Politeknik Negeri Medan
  • Habibi Ramdani Safitri Politeknik Negeri Medan
DOI: https://doi.org/10.35447/jikstra.v2i2.237
Keywords: face mask detection, tensorflow, face detection, CNN

Abstract

Recent COVID-19 pandemic has cause change in human habit. Wearing a mask is a must health protocol according to Ministry of Health regulation. The regulation was announced since the pandemic shows significance rose in cases since May 2020. By using face recognition algorithm in Tensorflow by using CNN, this research purpose is to create an automatically access control gate that can filtered out people who wear mask and not wearing mask. The system will be implemented using Raspberry Pi 4 to control a servo that can be used to block a passageway/gate. Masked face detection in this research can detect face without mask up to 99.5% correct. While this program has a high capability in detection face without mask, this program didn’t have high capability in detecting face using mask which result only 93% correct and worsening when the face using mask having picture/motif on the surface of the mask to only 53% correct detection. Overall accuracy reach for 76%.

Downloads

Download data is not yet available.

Author Biographies

Zakaria Sembiring, Politeknik Negeri Medan

Program studi teknik informatika

Habibi Ramdani Safitri, Politeknik Negeri Medan

Teknik Komputer dan Informatika

References

[1] Kementerian Kesehatan Republik Indonesia, "Keputusan Menteri Kesehatan Republik Indonesia Panduan Pencegahan Dan Pengendalian Corona Virus Disease 2019 (Covid-19) Di Tempat Kerja Perkantoran Dan Industri Dalam Mendukung Keberlangsungan Usaha Pada Situasi Pandemi," NO. HK.01.07/MENKES/328/2020, 2020.
[2] T. W. T. K. L. H. L. G. G. K. H. Jungkyu Lee, "Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network," 2020. [Online]. Available: https://arxiv.org/abs/2001.06268v2.
[3] P. Viola and M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," COMPUTER VISION AND PATTERN RECOGNITION, 2001.
[4] A. Maddineni and D. Hussian, "Live Object Detection api using Tensorflow on Raspberry Pi," INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS, p. 831, 2 April 2018.
[5] A. Y. W. d. R. S. I Wayan Suartika E. P, "Klasifikasi Citra Menggunakan Convolutional Neural Network (Cnn) pada Caltech 101," JURNAL TEKNIK ITS Vol. 5, No. 1, pp. A65-A69, 2016.
[6] A. S. A. F. Kamal Hasan Mahmud, "Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network," e-Proceeding of Engineering Vol.6, No.1 April 2019, pp. 2127-2136, 2019.
[7] X.-L. Xia, C. Xu and B. Nan, "Facial Expression Recognition Based on TensorFlow Platform," in ITM Web of Conferences 2017 International Conference on Information Science and Technology (IST 2017), Wuhan, Hubei, China, March 24-26, 2017, 2017.
[8] S. Raschka and V. Mirjalili, Machine Learning and Deep Learning with Python,, Birmingham: Packt Publishing Ltd., 2019.
[9] A. A. P. B. e. Mart´ın Abadi, "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," 09 09 2015. [Online]. Available: https://www.tensorflow.org/about/bib#large-scale_machine_learning_on_heterogeneous_distributed_systems.
[10] R. Pi, "Raspberry Pi 4," 02 06 2020. [Online]. Available: https://www.raspberrypi.org/products/raspberry-pi-4-model-b/.
[11] X. Z. S. R. J. S. Kaiming He, "Deep Residual Learning for Image Recognition," 2015. [Online]. Available: https://arxiv.org/abs/1512.03385v1.
Published
2021-04-01
Issue
Vol 2 No 2 (2020): Edisi Oktober
Section
Articles

Universitas Harapan Medan