Face Recognition using PCA and Eigen face approach

Abstract

Face is a complex, multidimensional structure and needs a good computing technique for detection and recognition. Our approach treats face as a two-dimensional recognition problem. In this scheme face recognition is done by Principal Component Analysis (PCA). Face images are projected onto a face space that encodes best variation among known face images. the face space is defined by Eigen face which are Eigen vectors of the set of faces, which may not correspond to the general facial features such as eyes, nose, lips. The Eigen face approach uses PCA for recognition of the face images. the system performs by projecting pre extracted face image onto a set of face space that represent significant variations among known face images. face will be categorized as “known” or “unknown” face after matching with the present database. If the user is new to the face recognition system then his/her template will be stored in the database else matched against the templates stored in the database. The variable reducing theory of PCA accounts for the smaller face space than the training set of face.