Set of eigenvectors used in the computer vision problem of human face recognition
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.
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. The approach...
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associated with a large set of normalized pictures of faces are called eigenfaces; this is an example of principal component analysis. They are very useful...
component analysis (PCA). The PCA method of face detection is also known as Eigenface and was developed by Matthew Turk and Alex Pentland. Turk and Pentland...
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ISSN 2448-6736. Jun Zhang; Yong Yan; Lades, M. (1997). "Face recognition: Eigenface, elastic matching, and neural nets". Proceedings of the IEEE. 85 (9):...
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applied to the low-resolution face image. By selecting the number of "eigenfaces", we can extract amount of facial image information of low resolution...
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