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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Hilman Mujahid Purnama
Atika Nur Rahmawati
Abdur Rohman Harits Martawireja

Abstract

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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How to Cite
Purnama, H. M., Rahmawati, A. N., & Harits Martawireja, A. R. (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. Retrieved from https://jtrm.polman-bandung.ac.id/index.php/JTRM/article/view/30
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