Eigen Face Reconstruction and Recognition

Face recognition using Eigen faces is an approach to the detection and identification of human faces and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space `face space' that best encodes the variation among known face images. The face space is defined by the `Eigen faces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. Eigen faces are obtained from Eigen vectors of an image which is a principle component of analysis. The Principal Component Analysis (PCA) is one of the most successful techniques that have been used in image recognition and compression. Face recognition has many applicable areas. Moreover, it can be categorized into face identification, face classification, or sex determination. The most useful applications contain crowd surveillance, video content indexing, etc.,