Abstract

Faces can be represented efficiently as a weighted linear combination of the eigenvectors of a covariance matrix of face images. It has also been shown [ J. Opt. Soc. Am.4, 519– 524 ( 1987)] that identifiable faces can be made by using only a subset of the eigenvectors, i.e., those with the largest eigenvalues. This low-dimensional representation is optimal in that it minimizes the squared error between the representation of the face image and the original face image. The present study demonstrates that, whereas this low-dimensional representation is optimal for identifying the physical categories of face, like sex, it is not optimal for recognizing the faces (i.e., discriminating known from unknown faces). Various low-dimensional representations of the faces in the higher dimensions of the face space (i.e., the eigenvectors with smaller eigenvalues) provide better information for face recognition.

Keywords

Eigenvalues and eigenvectorsRepresentation (politics)Face (sociological concept)Facial recognition systemMathematicsPattern recognition (psychology)Artificial intelligenceCovariance matrixSpace (punctuation)Computer scienceMatrix (chemical analysis)Image (mathematics)Computer visionAlgorithmPhysics

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Publication Info

Year
1993
Type
article
Volume
10
Issue
3
Pages
405-405
Citations
232
Access
Closed

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Alice J. O’Toole, Kenneth A. Deffenbacher, Dominique Valentin et al. (1993). Low-dimensional representation of faces in higher dimensions of the face space. Journal of the Optical Society of America A , 10 (3) , 405-405. https://doi.org/10.1364/josaa.10.000405

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DOI
10.1364/josaa.10.000405