Showing papers on "Eigenface published in 1990"

Application of the Karhunen-Loeve procedure for the characterization of human faces

Abstract: The use of natural symmetries (mirror images) in a well-defined family of patterns (human faces) is discussed within the framework of the Karhunen-Loeve expansion This results in an extension of the data and imposes even and odd symmetry on the eigenfunctions of the covariance matrix, without increasing the complexity of the calculation The resulting approximation of faces projected from outside of the data set onto this optimal basis is improved on average >

Face Recognition without Features.

Abstract: We have developed a near-real-time computer system which can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. Our approach treats the face recognition problem as an intrinsically twodimensional recognition problem, taking advantage of the fact that faces are are normally upright and thus may be described by a small set of 2-D 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 (principal componmt,s) of the set of faces; they do not necessarily correspontl to features such as eyes, ears, and noses.