The eigenface approach began with a search for a low-dimensional representation of face images. Sirovich and Kirby (1987) showed that principal component analysis could be used on a collection of face images to form a set of basis features. These basis images, known as eigenpictures, could be linearly combined to reconstruct images in the original training set. If the training set consists of "M" images, principal component analysis could form a basis set of "N" images, where "N < M". The reconstruction error is reduced by increasing the number of eigenpictures; however, the number needed is always chosen less than "M". For example, if you need to generate a number of "N" eigenfaces for a training set of "M" face images, you can say that each face image can be made up of "proportions" of all the "K" "features" or eigenfaces: Face image1 = (23% of E1) + (2% of E2) + (51% of E3) + ... + (1% En).