Pengenalan Citra Wajah Menggunakan Minimum Distance Classifier Berdasarkan Fitur Principal Component Analysis - Bella Adinda Putri; Saparudin Saparudin (JATISI (Jurnal Teknik Informatika dan Sistem Informasi), 2021)
This study presents a unique combination of feature extraction techniques and recognition methods that work well on more than one standard face dataset. The main focus in this research is how to obtain the features of each face image to distinguish faces from each other by applying the Principal Component Analysis (PCA) method as feature extraction, and the Minimum Distance Classifier as the recognition algorithm so that recognition results can be obtained. To achieve this goal, a literature study is needed to understand the concepts and theoretical basis in order to strengthen the assumptions of the Principal Component Analysis and Minimum Distance Classifier methods. The results of the recognition using ORL database get 97% accuracy, while the results of the recognition using YALE database get 94.6% accuracy. So it can be concluded that the combination of PCA and Minimum Distance Classifier can provide a quick and simple solution by increasing or without reducing standard accuracy.