Analisa Deteksi dan Pengenalan Wajah pada Citra dengan Permasalahan Visual

Abstract

Facial recognition is a significant part of criminal investigations because it may be used to identify the offender when the criminal's face is consciously or accidentally recorded on camera or video. However, a majority of these digital photos have poor picture quality, which complicates and lengthens the process of identifying a face image. The purpose of this study is to discover and identify faces in these low-quality digital photographs using the Principal Component Analysis (PCA) and Linear  Discriminant Analysis (LDA) face identification method and the Viola-Jones face recognition method. The success percentage for the labeled face in the wild (LFW) dataset is 63.33%, whereas the success rate for face94 is 46.66%, while LDA is only a maximum of 20% on noise and brightness. One of the names and faces from the dataset is displayed by the facial recognition system. The brightness of the image, where the facial item is located, and any new objects that have entered the scene have an impact on the success rate.