PENGENALAN CITRA WAJAH MENGGUNAKAN MINIMUM DISTANCE CLASSIFIER BERDASARKAN PRINCIPAL COMPONENT ANALYSIS

PUTRI, BELLA ADINDA and Saparudin, Saparudin and Samsuryadi, Samsuryadi (2020) PENGENALAN CITRA WAJAH MENGGUNAKAN MINIMUM DISTANCE CLASSIFIER BERDASARKAN PRINCIPAL COMPONENT ANALYSIS. Master thesis, Sriwijaya University.

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Abstract

The combination of Principal Component Analysis (PCA) and Minimum Distance Classifier methods in face recognition can work well on more than one standard datasets. The addition of pre-processing stages in the form of image enhancement in this studyis very important in improving the quality of the input image so that it can provide better accuracy than previous studies without slowing down the system. However, to achieve this goal, it is necessary to conduct a literature study to understand the concepts and theoretical basis in order to strengthen the assumptions of image enhancement techniques, Principal Component Analysis as feature extraction method, and Minimum Distance Classifier as recognition method. Recognition result with ORL database get an accuracy of 97%, while recognition result with YALE database get an accuracy of 94.6%. So it can be concluded that the addition of image enhancement techniques in the combination of the Principal Component Analysis and Minimum Distance Classifier methods can provide a fast and simple solution by increasing or without reducing its standard accuracy.

Item Type: Thesis (Master)
Uncontrolled Keywords: Pengenalan Wajah, Image Enhancement, Ekstraksi Ciri, Principal Component Analysis, Minimum Distance Classifier
Subjects: T Technology > T Technology (General) > T1-995 Technology (General)
Divisions: 09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User: Bella Adinda Putri
Date Deposited: 15 Nov 2021 01:30
Last Modified: 15 Nov 2021 01:30
URI: http://repository.unsri.ac.id/id/eprint/57345

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