Face Recognition Using PCA

Our approach rates the face recognition problem as an intrinsically two-dimensional (2D) recognition problem ratherthan requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2D characteristic views. The system functions by Projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as “eigenfaces”, because they are the eigenvectors (Principle components) of the set of faces. They do not necessarily correspond to the features such as eyes, ears and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features and so to recognize a Particular face it is necessary only to compare these weights to those of known individuals.