Eigenfaces are a set of eigenvectors used in the computer vision problem of human face recognition. These eigenvectors are derived from the covariance matrix of the probability distribution of the high-dimensional vector space of possible faces of human beings. Many authors prefer the term eigenimage rather than eigenface, as the technique has been used for handwriting, lip reading, voice recognition, and medical imaging.
In layman's terms, eigenfaces are a set of "standardized face ingredients", derived from statistical analysis of many pictures of faces. Any human face can be considered to be a combination of these standard faces. One person's face might be made up of 10% from face 1, 24% from face 2 and so on. This means that if you want to record someone's face for use by face recognition software you can use far less space than would be taken up by a digitised photograph.
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