Face Detection using Principal Component Analysis in Real Time Database

Abstract

Face Recognition is the process of identification of a person by their facial image. In this system, a holistic Principal Component Analysis (PCA) based method, namely Eigenface method is studied and implemented on the Faces 94 database. This approach treats face recognition as a two-dimensional recognition problem. Face images are projected onto a face space that encodes best variation among known face images. The face space is defined by eigenface which are eigenvectors of the set of faces, which may not correspond to general facial features such as eyes, nose, and lips. Face will be categorized as known or unknown face after matching with the present database. Experimental results in this thesis showed that an accuracy of 98.8158% was achieved. The variable reducing theory of PCA accounts for the smaller face space than the training set of face.