  This paper describes a novel face identification method that combines the
eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in
order to reduce the dimensionality of the input image, and a neural net
classifier that performs the identification process. The method presented
recognizes faces in the presence of variations in facial expression, facial
details and lighting conditions. A recognition rate of more than 87% has been
achieved, while the classical method of Turk and Pentland achieves a 75.5%.
