An eigenface (/ˈaɪɡən-/ EYE-gən-) is the name given to a set of eigenvectors when used in the computer vision problem of human face recognition.[1] The approach of using eigenfaces for recognition was developed by Sirovich and Kirby and used by Matthew Turk and Alex Pentland in face classification.[2][3] The eigenvectors are derived from the covariance matrix of the probability distribution over the high-dimensional vector space of face images. The eigenfaces themselves form a basis set of all images used to construct the covariance matrix. This produces dimension reduction by allowing the smaller set of basis images to represent the original training images. Classification can be achieved by comparing how faces are represented by the basis set.
- ^ Navarrete, Pablo; Ruiz-Del-Solar, Javier (November 2002). "Analysis and Comparison of Eigenspace-Based Face Recognition Approaches" (PDF). International Journal of Pattern Recognition and Artificial Intelligence. 16 (7): 817–830. CiteSeerX 10.1.1.18.8115. doi:10.1142/S0218001402002003. S2CID 7130804.
- ^ L. Sirovich; M. Kirby (1987). "Low-dimensional procedure for the characterization of human faces". Journal of the Optical Society of America A. 4 (3): 519–524. Bibcode:1987JOSAA...4..519S. doi:10.1364/JOSAA.4.000519. PMID 3572578.
- ^ Turk, Matthew A; Pentland, Alex P (1991). Face recognition using eigenfaces (PDF). Proc. IEEE Conference on Computer Vision and Pattern Recognition. pp. 586–591. doi:10.1109/cvpr.1991.139758. ISBN 0-8186-2148-6.