Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. The approach of using eigenfaces for recognition was developed by Sirovich and Kirby (1987) and used by Matthew Turk and Alex Pentland in face classification. It is considered the first successful example of facial recognition technology. These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings.
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... Facial recognition was the source of motivation behind the creation of eigenfaces ... For this use, eigenfaces have advantages over other techniques available, such as the system's speed and efficiency ... Using eigenfaces is very fast, and able to functionally operate on lots of faces in very little time ...