DOI: https://doi.org/10.14710/jtsiskom.2020.13590

Sistem pengenalan wajah dengan algoritme PCA-GA untuk keamanan pintu rumah pintar menggunakan Rasberry Pi

Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi

Universitas Negeri Semarang, Indonesia

Received: 12 Dec 2019; Revised: 18 May 2020; Accepted: 25 May 2020; Available online: 7 Jun 2020; Published: 31 Jul 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under http://creativecommons.org/licenses/by-sa/4.0.

Citation Format:
Abstract
Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.
Fulltext View|Download Email colleagues
Keywords: face recognition; genetic algorithm; principal component analysis; raspberry pi; smart home system
Funding: Lembaga Penelitian dan Pengabdian Masyarakat (LP2M), Universitas Negeri Semarang under contract 32.11.4/UN37/PPK.4.5/2018

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Most viewed articles
Decision Support System for Thesis Graduation Recommendation Using AHP-TOPSIS Method Perancangan Sistem Informasi Akademik Berbasis Mobile Web Studi Kasus di Program Studi Sistem Komputer Universitas Diponegoro Sistem Pengendali Peralatan Elektronik dalam Rumah secara Otomatis Menggunakan Sensor PIR, Sensor LM35, dan Sensor LDR K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes Perancangan Sistem Informasi Rekam Medis Rawat Inap Rumah Sakit Berbasis Web More articles
Most cited articles
Intelligent System of Parking Reservation and Monitoring on Campus using Internet of Things Concept Implementasi Algoritma Kriptografi RSA untuk Enkripsi dan Dekripsi Email Speed Bump Enforcement System Based on Vehicle Speed Classified by Haar Cascade Classifier Decision Support System for Subsidizing the Repair Cost of Containers Damage Using Naive Bayes Desain dan Implementasi Sistem Pembelajaran Jarak Jauh Di Program Studi Sistem Komputer More cited articles
  1. K. G. Hanssen and S. J. Darby, “‘Home is where the smart is’? evaluating smart home research and approaches against the concept of home,” Energy Research & Social Science, vol. 37, pp. 94–101, 2018. doi: 10.1016/j.erss.2017.09.037
  2. D. Mocrii, Y. Chen, and P. Musilek, “IoT-based smart homes : a review of system architecture, software, communications, privacy and security,” Internet of Things, vol. 1, no. 2, pp. 81–98, 2018. doi: 10.1016/j.iot.2018.08.009
  3. K. Lian, S. Hsiao, and W. Sung, “Smart home safety handwriting pattern recognition with innovative technology,” Computers and Electrical Engineering, vol. 40, pp. 1123–1142, 2014. doi: 10.1016/j.compeleceng.2014.02.010
  4. T. S. Gunawan, M. H. H. Gani, F. D. A. Rahman, and M. Kartiwi, “Development of face recognition on Raspberry Pi for security enhancement of smart home system,” Indonesian Journal of Electrical Engineering and Informatics, vol. 5, no. 4, pp. 317–325, 2017. doi: 10.11591/ijeei.v5i4.361
  5. K. Dhondge, K. Ayinala, B. Choi, and S. Song, “Infrared optical wireless communication for smart door locks using smartphones,” in 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks, Hefei, China, Dec. 2016, pp. 251–257. doi: 10.1109/MSN.2016.047
  6. M. S. Hadis, E. Palantei, A. A. Ilham, and A. Hendra, “Design of smart lock system for doors with special features using bluetooth technology,” in 2018 International Conference on Information and Communications Technology, Yogyakarta, Indonesia, Mar. 2018, pp. 396–400. doi: 10.1109/ICOIACT.2018.8350767
  7. L. Kamelia, M. R. Effendi, and D. F. Pratama, “Integrated smart house security system using sensors and RFID (door and lighting automation),” in 4thInternational Conference on Wireless and Telematics, Nusa Dua, Indonesia, Jul. 2018, pp. 1-5. doi: 10.1109/ICWT.2018.8527803
  8. H. Ai and X. Cheng, “Research on embedded access control security system and face recognition system,” Measurement, vol. 123, pp. 309–322, 2018. doi: 10.1016/j.measurement.2018.04.005
  9. H. Zhi and S. Liu, “Face recognition based on genetic algorithm,” Journal of Visual Communication and Image Representation., vol. 58, pp. 495–502, 2019. doi: 10.1016/j.jvcir.2018.12.012
  10. N. Meenakshi, M. Monish, K. J. Dikshit, and S. Bharath, “Arduino based smart fingerprint authentication system,” in 1st International Conference on Innovations in Information and Communication Technology, Chennai, India, Apr. 2019, pp. 1-7. doi: 10.1109/ICIICT1.2019.8741459
  11. R. Shelke and S. B. Bagal, “Iris recognition system : a novel approach for biometric authentication,” in 3th International Conference on Computing, Communication, Control and Automation, Pune, India, Aug. 2017, pp. 1-5. doi: 10.1109/ICCUBEA.2017.8463819
  12. M. Wang, E. J. Mcintee, G. Cheng, Y. Shi, P. W. Villalta, and S. S. Hecht, “Identification of DNA adducts of acetaldehyde,” Chemical Research in Toxicol, vol. 13, no. 11, pp. 1149–1157, 2000. doi: 10.1021/tx000118t
  13. D. Dinh, J. T. Kim, and T. Kim, “Hand gesture recognition and interface via a depth imaging sensor for smart home appliances,” Energy Procedia, vol. 62, pp. 576–582, 2014. doi: 10.1016/j.egypro.2014.12.419
  14. J. F. Ajao, D. O. Olawuyi, and O. O. Odejobi, “Yoruba handwritten character recognition using freeman chain code and k-nearest neighbor classifier,” Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 4, pp. 129–134, 2018. doi: 10.14710/jtsiskom.6.4.2018.129-134
  15. R. Subban, D. Mankame, S. Nayeem, P. Pasupathi, and S. Muthukumar, “Genetic algorithm based human face recognition,” in International Conference on Advances in Communication, Network, and Computing, Chennai, India, Feb. 2014, pp. 417–426
  16. T. O. Majekodunmi and F. E. Idachaba, “A review of the fingerprint, speaker recognition, face recognition and iris recognition based biometric identification technologies,” in Proceedings of The World Congress on Engineering 2011, London, UK, Jul. 2011, pp. 1-7
  17. D. Shah and V. Bharadi, “IoT based biometrics implementation on Raspberry Pi,” in International Conference on Communication, Computing and Virtualization, Mumbai, India, Feb. 2016, pp. 328–336. doi: 10.1016/j.procs.2016.03.043
  18. C. Zhou, L. Wang, Q. Zhang, and X. Wei, “Face recognition based on PCA and logistic regression analysis,” Optik, vol. 125, pp. 5916–5919, 2014. doi: 10.1016/j.ijleo.2014.07.080
  19. L. Chengyuan, Z. Ting, D. Dongsheng, and L. Chongshan, “Design and application of compound kernel-PCA algorithm in face recognition,” in 35th Chinese Control Conference, Chengdu, China, Jul. 2016, pp. 4122–4126. doi: 10.1109/ChiCC.2016.7553997
  20. S. S. Meher and P. Maben, “Face recognition and facial expression identification using PCA,” in 2014 IEEE International Advance Computing Conference, Gurgaon, India, Feb. 2014, pp. 1093–1098. doi: 10.1109/IAdCC.2014.6779478
  21. E. I. Abbas, M. E. Safi, and K. S.Rijab, “Face recognition rate using different classifier methods based on PCA,” in 2017 International Conference on Current Research in Computer Science and Information Technology, Slemani, Iraq, Apr. 2017, pp. 37–40. doi: 10.1109/CRCSIT.2017.7965559
  22. B. S. D. Mangala and N. B. Prajwala, “Facial expression recognition by calculating euclidian distance for eigen faces using PCA,” in International Conference on Communication and Signal Processing, India, April. 3-5, 2018, pp. 244–248
  23. A. L. Ramadhani, P. Musa, and E. P. Wibowo, “Human face recognition application using PCA and eigenface approach,” in 2017 Second International Conference on Informatics and Computing, Jayapura, Indonesia, Nov. 2017, pp. 1-5. doi: 10.1109/IAC.2017.8280652
  24. C. Li, J. Liu, A. Wang, and K. Li, “Matrix reduction based on generalized PCA method in face recognition,” in 2014 International Conference on Digital Home, Guangzhou, China, Nov. 2014, pp. 35–38. doi: 10.1109/ICDH.2014.14
  25. O. Somantri and S. Wiyono, “Peningkatan akurasi klasifikasi tingkat penguasaan materi bahan ajar menggunakan jaringan syaraf tiruan dan algoritma genetika,” Jurnal Teknologi dan Sistem Komputer, vol. 5, no. 4, pp. 147–152, 2017. doi: 10.14710/jtsiskom.5.4.2017.147-152
  26. W. H. Al-arashi, H. Ibrahim, and S. A. Suandi, “Optimizing principal component analysis performance for face recognition using genetic algorithm,” Neurocomputing, vol. 128, pp. 415–420, 2014. doi: 10.1016/j.neucom.2013.08.022
  27. “Raspberry Pi 3 model B,” raspberrypi.org. [Online]. Available: https://www.raspberrypi.org/ products/raspberry-pi-3-model-b/. [Accessed: 26-Sep-2019]
  28. K. Yelne, “Face recognition using Raspberry Pi,” Github, 2019. [Online]. Available: https://github.com/kunalyelne/Face-Recognition-using-Raspberry-Pi/tree/master/. [Accessed: 26-Sep-2019]
  29. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Conference on Computer Vision and Pattern Recognition 2001, Kauai, USA, Dec, 2001, pp. 1–9. doi: 10.1109/CVPR.2001.990517
  30. S. C. Ng, “Principal component analysis to reduce dimension on digital image,” Procedia Computer Science, vol. 111, pp. 113–119, 2017. doi: 10.1016/j.procs.2017.06.017
  31. A. Desiani, “Kajian pengenalan wajah dengan menggunakan metode face-ARG dan jaringan syaraf tiruan backpropagation,” Media Informatika., vol. 5, no. 2, pp. 99–111, 2007
  32. H. Simaremare and A. Kurniawan, “Perbandingan akurasi pengenalan wajah menggunakan metode LBPH dan eigenface dalam mengenali tiga wajah sekaligus secara real-time,” Jurnal Sains, Teknologi dan Industri., vol. 14, no. 1, pp. 66–71, 2016

Last update:

  1. Hybrid of DCT And PCA For Image Size Compression

    Heba Jabbar Hassan, Mousa K. Wali, Mohamed Ibrahim Shujaa. 2023 International Conference on Computer and Applications (ICCA), 2023. doi: 10.1109/ICCA59364.2023.10401790

  2. Application of portrait recognition system for emergency evacuation in mass emergencies

    Ke Xu. Journal of Intelligent Systems, 30 (1), 2021. doi: 10.1515/jisys-2021-0052

  3. Prototype of Iot-Based Keyless Security System For Motorcycle

    Adika Akbar Saputra, Vera Suryani, Andrian Rakhmatsyah. 2023 6th International Conference on Information and Communications Technology (ICOIACT), 2023. doi: 10.1109/ICOIACT59844.2023.10455891

Last update: 2024-11-13 16:28:08

  1. IoT and Sign Language System (SLS)

    Mouti S.. International Journal of Engineering Research and Technology, 13 (12), 2021.