Review Paper: Face Recognition Techniques in Digital Image Processing


  • Noviana Dewi Universitas Budi Luhur
  • Setyawan Widyarto Unisel


digital images, soil imagery, k-means clustering, apple crops, land


Purpose: This paper aims to explore the face recognition process, the accuracy of the face recognition system, and the system can recognize face in real time. Background: Face recognition is a biometric technique that allows a computer to recognize faces by comparing the input image with the image from the provided database. Currently, face recognition has been widely used in various fields and one of these usages is for security systems. In a security system, a high accuracy and very good performance in the face recognition process is needed. Design/Methodology/Approach: . There are many methods used in face recognition systems, some of them are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Nearest Neighbor, Gabor Wavelet and Boosted Cascade Classifier. There are several factors that influence the success of the face recognition system, namely the distance of the face to the device such as camera, lighting, the number of images of people's faces stored and the performance of the computer used. Results/Findings: The accuracy and performance values from the PCA, LDA, K-Nearest Neighbor, Gabor Wavelet, Boosted Cascade Classifier method with several test samples according to the factors that influence success in the face recognition system will be exposed. Conclusion and Implications: This paper is expected to make readers who want to build systems with face recognition features easier to determine which method will produce the appropriate level of accuracy without reducing the performance of the system built.


Arhandi, P. P., Rosiani, U. D., Prasetyawati, A., & Choirina, P. (2018). Sistem Pengenalan Wajah Untuk Keamanan Folder. 4, 268–273.

Fandiansyah, F., Sari, J. Y., & Ningrum, I. P. (2017). Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis Dan K Nearest Neighbor. Jurnal Informatika, 11(2).

Marti, N. W., Yota, K., & Aryanto, E. (2016). Prototipe Sistem Absensi Berbasis Face Recognition Dengan Metode Eigenface. Seminar Nasional Vokasi Dan Teknologi, 451–456.

Muliawan, M. R., Irawan, B., & Brianorman, Y. (2015). Metode Eigenface Pada Sistem Absensi. Jurnal Coding, Sistem Komputer Untan, 03(1), 41–50.

Pratiwi, N. W., Fauziah, F., Andryana, S., & Gunaryati, A. (2018). Deteksi Wajah Menggunakan Hidden Markov Model (HMM) Berbasis Matlab. STRING (Satuan Tulisan Riset Dan Inovasi Teknologi), 3(1), 44.

Puspaningrum, E. Y., & Saputra, W. S. J. (2018). Deteksi Wajah Dengan Boosted Cascade Classifier. SCAN - Jurnal Teknologi Informasi Dan Komunikasi, 13(3), 1–4.

Salamun, & Wazir, F. (2016). RABIT : Jurnal Teknologi dan Sistem Informasi Univrab Volume 1 No . 2 | Juli 2016 : 48-60 ISSN CETAK : 2477-2062 ISSN ONLINE : 2502-891X SISTEM SECURITY WEBCAM DENGAN MENGGUNAKAN MICROSOFT VISUAL RABIT : Jurnal Teknologi dan Sistem Informasi Univrab Volum. 1(2), 48–60.

Sulistyo, G. B. (2018). Deteksi Dan Identifikasi Wajah Pada Citra Menggunakan Metode Principal Component Analysis Dan Gabor Wavelet Untuk Sistem Presensi Karyawan. ?????? ??????? ????????????, 2(1), 227–249.

Syuhada, F., Suta Wijaya, I. G. P., & Bimantoro, F. (2018). Pengenalan Wajah Untuk Sistem Kehadiran Menggunakan Metode Eigenface dan Euclidean Distance. Journal of Computer Science and Informatics Engineering (J-Cosine), 2(1), 64–69. Vol. 6 No. 10 (2020): Informatics - Digital Image Processing 29




How to Cite

Dewi, N. ., & Widyarto, S. (2021). Review Paper: Face Recognition Techniques in Digital Image Processing. Proceedings of the Informatics Conference, 6(10), 18-29. Retrieved from

Most read articles by the same author(s)

1 2 3 4 > >>