Perbandingan Algoritma Fisherface dan Algoritma Local Binary Pattern Untuk Pengenalan Wajah
Face recognition system is a system used to detect facial images, which is used to provide accuracy in a system used for access control for facilities that require such a security system. However, it is not uncommon to find problems in the face recognition process, such as the difficulty of the system to recognize faces if similar training data are found. In addition, there are difficulties in the face recognition process due to the lack of the number of images and/or poses of the training data images which results in the system not being optimal in recognizing faces. Several methods were proposed to create a reliable facial recognition system, such as Fisherface and Local Binary Pattern. The advantage of using the Fisherface method is that the processing time is relatively fast. This is because the fisherfaces process uses a matrix process. In addition to the fisherface method, the Local Binary Pattern method is also used in facial recognition systems. This method is also known to be quite easy and efficient in its application to obtain facial image characteristics. With the advantages and disadvantages of each of the above methods, the author will conduct a comparative test between the two methods to measure the level of accuracy of each method. In this study, two algorithms are used, namely the Fisherface algorithm and the Local Binary Pattern algorithm. Where the Fisherface algorithm is a derivative of FLD combined with (PCA). PCA is in charge of reducing input data to simplify and speed up the process and FLD is in charge of producing a distribution matrix to facilitate classification and recognition. In addition to the fisherface method, the Local Binary Pattern algorithm is also used in the face recognition system. LBP is defined as the comparison of the binary value of the pixel at the center of the image with the 8 values of the surrounding pixels so that the test image can be matched with the reference image. This research is useful for the development of facial recognition systems to be precise in the selection of methods, to minimize the occurrence of errors in the face recognition process. As well as getting a high level of accuracy and speed in facial recognition
A. B. S and H. Maulana, “Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis ( PCA ),” vol. 9, no. 2, pp. 166–175, 2016.
A. R. Putri, “Pengolahan Citra Dengan Menggunakan Web Cam Pada Kendaraan Bergerak Di Jalan Raya,” JIPI (Jurnal Ilm. Penelit. dan Pembelajaran Inform., vol. 1, no. 01, pp. 1–6, 2016, doi: 10.29100/jipi.v1i01.18.
D. Teknik, I. Universitas, M. Lhokseumawe, M. Teknik, and I. Universitas, “Pengenalan Bentuk Wajah Manusia Pada Citra Menggunakan Metode Fisherface,” TECHSI J. Penelit. Tek. Inform., vol. 15, pp. 1–11, 2015.
L. W. Alexander, S. R. Sentinuwo, A. M. Sambul, T. Informatika, U. Sam, and R. Manado, “Implementasi Algoritma Pengenalan Wajah Untuk Mendeteksi Visual Hacking,” J. Tek. Inform. Univ. Sam Ratulangi, vol. 11, no. 1, 2017, doi: 10.35793/jti.11.1.2017.16969.
R. Arlando Saragih, “Pengenalan Wajah Menggunakan Metode Fisherface,” J. Tek. Elektro, vol. 7, no. 1, pp. 50–61, 2007, doi: 10.9744/jte.7.1.50-62.
R. Amat, J. Y. Sari, and I. P. Ningrum, “Implementasi Metode Local Binary Patterns Untuk Pengenalan Pola Huruf Hiragana Dan Katakana Pada Smartphone,” JUTI J. Ilm. Teknol. Inf., vol. 15, no. 2, p. 152, 2017, doi: 10.12962/j24068535.v15i2.a612.
Suendri, “Implementasi Diagram UML (Unified Modelling Language) Pada Perancangan Sistem Informasi Remunerasi Dosen Dengan Database Oracle (Studi Kasus: UIN Sumatera Utara Medan),” J. Ilmu Komput. dan Inform., vol. 3, no. 1, pp. 1–9, 2018.
P. Pt and A. P. M. Rent, “No Title,” vol. 2, no. 2, pp. 64–77, 2018.
A. Andrew, J. L. Buliali, and A. Y. Wijaya, “Deteksi Kecepatan Kendaraan Berjalan di Jalan Menggunakan OpenCV,” J. Tek. ITS, vol. 6, no. 2, 2017, doi: 10.12962/j23373539.v6i2.23489.
B. Cahyono, “Penggunaan Software Matrix Laboratory (Matlab) Dalam Pembelajaran Aljabar Linier,” Phenom. J. Pendidik. MIPA, vol. 3, no. 1, p. 45, 2016, doi: 10.21580/phen.2013.3.1.174.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Perbandingan Algoritma Fisherface dan Algoritma Local Binary Pattern Untuk Pengenalan Wajah
Copyright (c) 2022 Nurul Amalia
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).