Perancangan Sistem Pengenalan Wajah untuk Keamanan Ruangan Menggunakan Metode Local Binary Pattern Histogram

Sunardi Sunardi, Anton Yudhana, Muhamad Alwi Talib

Abstract


Saat ini telah banyak dikembangkan sistem pengamanan akses masuk ke ruangan dengan verifikasi identitas menggunakan kunci, kartu, dan sebagainya. Namun keterbatasan manusia dalam mengingat benda sehingga kadang terdapat kejadian tertinggal atau terlupa kombinasi angka atau password yang mengakibatkan tidak dapat untuk mengakses ruangan. Teknik verifikasi wajah diperlukan untuk mengakses ruangan dengan teknologi biometrik yang handal dan efisien tanpa harus mengingat objek seperti kunci, kartu, kata sandi, atau pin. Oleh karena itu tujuan penelitian adalah membuat rancang bangun sistem keamanan akses ruangan menggunakan face recognition menggunakan metode LBPH berbasis Raspberry Pi. Sistem yang dikembangkan terdiri dari dua bagian, yaitu alat yang dipasang pada Raspberry Pi utama yang menjadi otak kamera dan aplikasi Telegram pada smartphone. Kamera dapat mengenali wajah pengguna dan beberapa orang yang dapat mengakses ruangan. Jika kamera tidak mengenali wajah orang yang terdeteksi maka kamera akan mengambil gambar dan mengirimkannya ke pemilik rumah melalui Telegram sebagai notifikasi untuk tindakan lebih lanjut terhadap kedatangan orang yang tidak dikenal.

Keywords


Haar-cascade; LBPH; Keamanan; Raspberry; Telegram

Full Text:

PDF

References


A. Yudhana, S. Sunardi, and P. Priyatno, “Perancangan Pengaman Pintu Rumah Berbasis Sidik Jari Menggunakan Metode UML,” J. Teknol., vol. 10, no. 2, pp. 131–138, 2018, [Online]. Available: https://dx.doi.org/10.24853/jurtek.10.2.131-138.

A. Yudhana, S. Sunardi, and P. Priyatno, “Development of Door Safety Fingerprint Verification Using Neural Network,” J. Phys. Conf. Ser., vol. 1373, no. 1, 2019, doi: 10.1088/1742-6596/1373/1/012053.

N. A. Hussein and I. Al Mansoori, “Smart Door System for Home Security Using Raspberry Pi3,” 2017 Int. Conf. Comput. Appl. ICCA 2017, pp. 395–399, 2017, doi: 10.1109/COMAPP.2017.8079785.

A. Nag, J. N. Nikhilendra, and M. Kalmath, “IOT Based Door Access Control Using Face Recognition,” 2018 3rd Int. Conf. Converg. Technol. I2CT 2018, pp. 1–3, 2018, doi: 10.1109/I2CT.2018.8529749.

Soe Sandar | Saw Aung Nyein Oo, “Development of a Secured Door Lock System Based on Face Recognition Using Raspberry Pi and GSM Module,” Int. J. Trend Sci. Res. Dev., vol. 3, no. 5, pp. 357–361, 2019, [Online]. Available: http://www.ijtsrd.com/papers/ijtsrd25280.pdf.

Z. Zhu and Y. Cheng, “Application of Attitude Tracking Algorithm for Face Recognition Based on OpenCV in the Intelligent Door Lock,” Comput. Commun., vol. 154, no. 900, pp. 390–397, 2020, doi: 10.1016/j.comcom.2020.02.003.

R. A. Nadafa, S. M. Hatturea, V. M. Bonala, and S. P. Naikb, “Home Security Against Human Intrusion Using Raspberry Pi,” Procedia Comput. Sci., vol. 167, no. Iccids 2019, pp. 1811–1820, 2020, doi: 10.1016/j.procs.2020.03.200.

M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cogn. Neurosci., vol. 3, no. 1, pp. 71–86, Jan. 1991, doi: 10.1162/jocn.1991.3.1.71.

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face Recognition by Independent Component Analysis,” IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450–1464, Nov. 2002, doi: 10.1109/TNN.2002.804287.

J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, “Face recognition using LDA-based algorithms,” IEEE Trans. Neural Networks, vol. 14, no. 1, pp. 195–200, 2003, doi: 10.1109/TNN.2002.806647.

B. Heisele, P. Ho, and T. Poggio, “Face recognition With Support Vector Machines: Global Versus Component-based Approach,” Proc. IEEE Int. Conf. Comput. Vis., vol. 2, no. July, pp. 688–694, 2001, doi: 10.1109/ICCV.2001.937693.

A. V. Nefian and M. H. Hayes, “Hidden Markov Models for Face Recognition,” in Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP ’98 (Cat. No.98CH36181), 1998, vol. 5, no. 4, pp. 2721–2724, doi: 10.1109/ICASSP.1998.678085.

T. K. Vamsi, K. C. Sai, and M. Vijayalakshmi, “Face Recognition Based Door Unlocking System Using Raspberry Pi,” nternational J. Adv. Res. Ideas Innov. Technol., vol. 5, no. 2, pp. 1320–1324, 2019, [Online]. Available: https://www.ijariit.com/manuscripts/v5i2/V5I2-1856.pdf.

S. Saifullah, S. Sunardi, and A. Yudhana, “Analisis Perbandingan Pengolahan Citra Asli dan Hasil Croping untuk Identifikasi Telur,” J. Tek. Inform. dan Sist. Inf., vol. 2, no. 3, pp. 341–350, 2016, doi: 10.28932/jutisi.v2i3.512.

P. B. Patel, V. M. Choksi, S. Jadhav, and M. B. Potdar, “Smart Motion Detection System Using Raspberry Pi,” Int. J. Appl. Inf. Syst., vol. 10, no. 5, pp. 37–40, Feb. 2016, doi: 10.5120/ijais2016451506.

N. Surantha and W. R. Wicaksono, “Design of Smart Home Security System using Object Recognition and PIR Sensor,” Procedia Comput. Sci., vol. 135, pp. 465–472, 2018, doi: 10.1016/j.procs.2018.08.198.

S. Desai and V. D. Pawar, “Smart Door Security System Using Raspberry Pi with Telegram,” Int. Res. J. Eng. Technol., vol. 6, no. 6, pp. 1400–1404, 2019, [Online]. Available: https://www.irjet.net/archives/V6/i6/IRJET-V6I6338.pdf.

M. Sajjad et al., “Raspberry Pi Assisted Face Recognition Framework for Enhanced Law-enforcement Services in Smart Cities,” Futur. Gener. Comput. Syst., vol. 108, pp. 995–1007, 2020, doi: 10.1016/j.future.2017.11.013.

M. I. Pure, A. Ma’arif, and A. Yudhana, “Alat Deteksi Detak Jantung Pada Atlet Maraton Menggunakan Raspberry,” vol. 7, no. 2, pp. 282–290, 2021.

Sunardi, A. Yudhana, and S. Saifullah, “Identity Analysis of Egg Based on Digital and Thermal Imaging: Image Processing and Counting Object Concept,” Int. J. Electr. Comput. Eng., vol. 7, no. 1, pp. 200–208, 2017, doi: 10.11591/ijece.v7i1.pp200-208.

A. Yudhana, Sunardi, and S. Saifullah, “Segmentation Comparing Eggs Watermarking Image and Original Image,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 47–53, 2017, doi: 10.11591/eei.v6i1.595.

P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1979, vol. 1, no. 10, pp. I-511-I–518, doi: 10.1109/CVPR.2001.990517.

M. Coskun, A. Ucar, O. Yildirim, and Y. Demir, “Face Recognition Based on Convolutional Neural Network,” in 2017 International Conference on Modern Electrical and Energy Systems (MEES), Nov. 2017, vol. 54, no. 5, pp. 376–379, doi: 10.1109/MEES.2017.8248937.

X. M. Zhao and C. B. Wei, “A Real-time Face recognition System Based on the Improved LBPH Algorithm,” 2017 IEEE 2nd Int. Conf. Signal Image Process. ICSIP 2017, vol. 2017-Janua, pp. 72–76, 2017, doi: 10.1109/SIPROCESS.2017.8124508.

A. Yudhana, S. Sunardi, and S. Saifullah, “Perbandingan Segmentasi Pada Citra Asli dan Citra Komprese Wavelet untuk Identifikasi Telur,” Ilk. J. Ilm., vol. 8, no. 3, pp. 190–196, Dec. 2016, doi: 10.33096/ilkom.v8i3.75.190-196.




DOI: http://dx.doi.org/10.22441/jte.2022.v13i2.010

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Jurnal Teknologi Elektro

Publisher Address:
Teknik Elektro, Fakultas Teknik, Universitas Mercu Buana
Jl. Raya Meruya Selatan, Kembangan, Jakarta 11650
Tlp./Fax: +62215871335
Email: jte@mercubuana.ac.id
Website of Electrical Engineering
http://teknikelektro.ft.mercubuana.ac.id

p-ISSN : 2086-9479
e-ISSN : 2621-8534
Jurnal URL : http://publikasi.mercubuana.ac.id/index.php/jte
Jurnal DOI: 10.22441/jte

 

Lisensi Creative Commons
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.

Web
Analytics Made Easy - StatCounter
View My Stats

The Journal is indexed by: