Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor)

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Abdur Rohman Harits Martawireja
Hilman Mujahid Purnama
Atika Nur Rahmawati


many applications apply it, both in the commercial and law enforcement fields. Face recognition is a system that used to identify based on the facial characteristic of a biometric-based person which has high accuracy. Face Recognition can be used in security system. Many methods are used in face recognition for security system, but in this paper will discuss only about 2 methods, there are Two Dimensial Principal Component Analysis and Kernel Fisher’s Discriminant Analysis and each methods use K-Nearest Neighbor for the class classification. For the testing system, both of them use the cross validation testing method. From the previous research, the face recognition accuracy with 5-folds cross validation of Two Dimensial Principal Component Analysis method is 88,73%, while the accuracy with 2-folds cross validation of it is 89,25%. And the average of Kernel Fisher Discriminant Analysis’ accuracy is about 83,10%.


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Harits Martawireja, A. R., Purnama, H. M., & Rahmawati, A. N. (2020). Analisis Metode Pengenalan Wajah Two Dimensial Principal Component Analysis (2DPCA) dan Kernel Fisher Discriminant Analysis Menggunakan Klasifikasi KNN (K- Nearest Neighbor). Jurnal Teknologi Dan Rekayasa Manufaktur , 2(2), 29-38.


[1] E. B. Utomo, “Pengenalan Wajah Wanita Berkerudung Menggunakan Metode 2DPCA dan K-Nearest Neighbor,” pp. 1–7, 2014.
[2] Lina and A. J. Feriyansah, “Sistem Pengenalan Wajah Dengan Metode 2D-PCA,” J. Pendidik. Teknol. Komun. Terap., no. July 2012, p. 138, 2012.
[3] R. T. Wahyuningrum and F. Damayanti, “Efficient kernel-based two-dimensional principal component analysis for smile stages recognition,” Telkomnika, vol. 10, no. 1, pp. 113–118, 2012.
[4] J. Yang, D. Zhang, A. F. Frangi, and J. Y. Yang, “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 1, pp. 131–137, 2004.
[5] L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” J. Opt. Soc. Am. A, vol. 4, no. 3, p. 519, 1987.
[6] J. Shah, M. Sharif, M. Raza, and A. Azeem, “A Survey: Linear and Nonlinear PCA Based Face Recognition Techniques,” Int. Arab J. Inf. Technol., no. November, 2013.
[7] K. Liu, Y. Q. Cheng, and J. Y. Yang, “Algebraic feature extraction for image recognition based on an optimal discriminant criterion,” Pattern Recognit., vol. 26, no. 6, pp. 903–911, 1993.
[8] J. Yang and J. Y. Yang, “From image vector to matrix: A straightforward image projection technique-IMPCA vs. PCA,” Pattern Recognit., vol. 35, no. 9, pp. 1997–1999, 2002.
[9] Informatikalogi, “Algoritma K-Nearest Neighbor (K-NN),” 2017. [Online]. Available:
[10] V. S. Vijayalakshmi, B. Shwetha, and S. V Sathyanarayana, “Image Classifier based Digital Image Forensic Detection-A Review and Simulations,” Int. Conf. Emerg. Res. Electron. Comput. Sci. Technol., 2015.
[11] I. Setyawan, A. F. Putra, I. K. Timotius, and A. A. Febrianto, “Face recognition using Kernel Fisher’s Discriminant Analysis and nearest neighbor,” Proc. 2011 6th Int. Conf. Telecommun. Syst. Serv. Appl. TSSA 2011, pp. 5–7, 2011.