Teknik Pengenalan Wajah Menggunakan Model Ekstraksi Fitur Citra Digital
DOI:
https://doi.org/10.37366/jpcs.v2i2.1465Keywords:
face image, eiginfaces, image extraction, face recognitionAbstract
The use of information technology has been widely encountered in our daily life. Not only to process data, to record tools but also to identify and recognize human characteristics. This is called biometric technology. This technology identifies the unique and permanent parts of the human body such as fingerprints, eyes, and the shape of the human face. To identify and recognize human faces, use facial image processing and analysis, such as determining the component regions of the human face and their characteristics. Splitting the face image into facial components, then extracting it into the features of the eyes, nose, mouth, and chin. The distance between each component is measured, then combined with other features to form facial semantics. The face can be categorized into the T Zone which consists of the forehead, eyes, nose and mouth. Eyes, nose, and mouth are the most unique facial components for facial recognition because they have unique facial recognition features. For the distance of the eye and mouth triangle feature, J1 – J3 shows that there are 140 unique data with the percentage value is 93.33%. The feature distance J4 – J6 also shows that there are 126 unique face images with a percentage value of 85%.
Keyword: face image, eiginfaces, image extraction, face recognition.
Downloads
References
Ahmad, N., & Hadinegoro, A. (2012). Metode Histogram Equaliization untuk Perbaikan Ciitra Diigital. Semiinar Nasiional Teknologii Informasii & Komuniikasi Teraapan (SEMANTIiK), 3(Semantik), 439–445. http://publikasi.dinus.ac.id/index.php/semantik/article/view/185
Budi, A., Suma’inna, S., & Maulana, H. (2016). Pengenalan Citra Wajah Sebagai Identifier Menggunakan Metode Principal Component Analysis (PCA). Jurnal Teknik Informatika, 9(2), 166–175. https://doi.org/10.15408/jti.v9i2.5608
Ciputra, A., Setiadi, D. R. I. M., Rachmawanto, E. H., & Susanto, A. (2018). Klasifikasi Tingkat Kematangan Buah Apel Manalagi Dengan Algoritma Naive Bayes Dan Ekstraksi Fitur Citra Digital. Simetris: Jurnal Teknik Mesin, Elektro Dan Ilmu Komputer, 9(1), 465–472. https://doi.org/10.24176/simet.v9i1.2000
Dana, T. O., Asni b, A., & Waruni, M. (2019). Identifikasi Wajah Dengan Segmentasi Warna Kulit Menggunakan Metode Viola Jones. Jurnal Teknik Elektro Uniba (JTE UNIBA), 4(1), 1–6. https://doi.org/10.36277/jteuniba.v4i1.47
Fandiansyah, F., Sari, J. Y., & Ningrum, I. P. (2017). Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor. Jurnal ULTIMATICS, 9(1), 1–9. https://doi.org/10.31937/ti.v9i1.557
Kita, D., Widodo, A. W., & Rahman, M. A. (2019). Ekstraksi Ciri pada Klasifikasi Tipe Kulit Wajah Menggunakan Metode Local Binary Pattern. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 3(8), 7938–7945.
Rohpandi, D., Sugiharto, A., & Jati, M. Y. S. (2018). Klasifikasi Citra Digital Berbasis Ekstraksi Ciri Berdasarkan Tekstur Menggunakan GLCM Dengan Algoritma K-Nearest Neighbor. Jurnal Informatika, vol 7 No 2(2), 79–86.
Suroso, & Ermaya, S. K. (2018). Pengenalan Citra Wajah dengan Metode Eigen Face Menggunakan Matlab 7.11.0.548. Jurnal IPSIKOM, 6(1).
Susim, T., & Darujati, C. (2021). Pengolahan Citra untuk Pengenalan Wajah (Face Recognition) Menggunakan OpenCV. Jurnal Syntax Admiration, 2(3), 534–545. https://doi.org/10.46799/jsa.v2i3.202
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Journal of Practical Computer Science
This work is licensed under a Creative Commons Attribution 4.0 International License.