Abstract
A Facial photos in today's era are widely used as a media for identity recognition, but not many computer applications provide identity recognition of face photos that contain the names of the photo owners, to make an a prototype the sistems use a KNN algorithm, this algorithm work is by classifying the closest object and grouping it on predetermined objects. In this paper, the object is a face photo where the KNN algorithm will be used to classify the facial patterns contained in the photo. The stages in pattern recognition, starting from preprocessing, feature extraction and then classification. In addition to using the KNN algorithm for data classification, photo of faces will be detected and stored the T-Zone area and frontal face. In this paper 11 images used for data testing and the accuracy will be calculated using a recognition algorithm. The results of this paper are a facial recognition program using python that can display faces with a validity level of 82%.
Highlights
Foto adalah citra yang didapatkan dari kamera, baik itu kamera dslr, kamera smartphone ataupun kamera cctv yang mempunyai fungsi merekam object pada waktu-waktu dan kejadian tertentu
A Facial photos in today's era are widely used as a media for identity recognition
many computer applications provide identity recognition of face photos that contain the names of the photo owners
Summary
Foto adalah citra yang didapatkan dari kamera, baik itu kamera dslr, kamera smartphone ataupun kamera cctv yang mempunyai fungsi merekam object pada waktu-waktu dan kejadian tertentu. Pattern recognition digunakan untuk mengetahui atau mengindentifikasi objek dari sebuah media yang pada penelitian ini menggunakan media foto [3]. Pada penelitian ini foto wajah akan diproses kemudian diambil koordinatnya menggunakan T-Zone area dan Frontal face kemudian jarak koordinat dihitung dengan menggunakan algoritma KNN. Algoritma KNN (K-Nearest Neighbor) merupakan teknik atau metode untuk melakukan klasifikasi terhadap object melalui data training dengan menggunakan jarak yaitu jaraknya paling dekat dengan objek tersebut [5], [6].
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