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PERBANDINGAN PERFORMA PENGGABUNGAN METODE LOCAL BINARY PATTERN HISTOGRAM (LBPH) DENGAN DETEKSI TEPI BERBASIS GRADIEN PADA SISTEM PENGENALAN WAJAH MANUSIA

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The use of the LBPH method in face recognition is the most efficient method compared to previous methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Elastic Bunch Graph Matching (EBGM). However, the performance of the LBPH method can be further improved with a combination of gradient-based edge detection methods. Sobel, Prewitt, and Robert's edge detection method serves as a provider of gradient values for each image pixel that will help in the LBP operation process. In this study, three classification processes were carried out using each combination of methods, namely Sobel-LBP, Prewitt-LBP, and Robert-LBP on the “Yale Faces Database” dataset. Each accuracy value generated is 89%, 87%, and 84%, while the LBPH method without using edge detection is 76%. The test results show that the combination of gradient-based edge detection methods with LBP operations can improve recognition performance with the highest accuracy value in Sobel-LBP, because the kernel size in the Sobel method is larger than that of Prewitt and Robert. However, the three edge detection methods have the same limitations, which are very sensitive to noise in the image.

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Inventory Code Barcode Call Number Location Status
2307006375T130895T1308952023Central Library (Referens)Available
Detail Information
Series Title
-
Call Number
T1308952023
Publisher
Indralaya : Prodi Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xix, 81 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
005.107
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Prodi Teknik Informatika
Identifikasi Sistem
Specific Detail Info
-
Statement of Responsibility
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