Fisherface uses linear discriminant analysis and is less sensitive to variation in lighting and pose of the face. The eigenfeatures method combines facial metrics (measuring distance between facial features) with the eigenface representation. Various extensions have been made to the eigenface method. Experiments in the original Eigenface paper presented the following results: an average of 96% with light variation, 85% with orientation variation, and 64% with size variation. This limits the application of such a system. An image of one subject under frontal lighting may have very different weights to those of the same subject under strong left lighting. The weights of each gallery image only convey information describing that image, not that subject. If the training set consists of M images, principal component analysis could form a basis set of N images, where N ϵ is not a face image. These basis images, known as eigenpictures, could be linearly combined to reconstruct images in the original training set. Sirovich and Kirby showed that principal component analysis could be used on a collection of face images to form a set of basis features. The eigenface approach began with a search for a low-dimensional representation of face images.
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