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Face recognition under various lighting condition's is discussed to cover cases when too few images are available for registration. This paper proposes decomposition of an eigenface into two orthogonal eigenspaces for realizing robust face recognition under such conditions. The decomposed eigenfaces consisting of two eigenspaces are constructed for each person even if only one image is available. A universal eigenspace called the canonical space (CS) plays an important role in creating the eigenspaces by way of decomposition, where CS is constructed a priori by principal component analysis (PCA) over face images of many people under many lighting conditions. In the registration stage, an input face image is decomposed to a projection image in CS and the residual of the projection. Then two eigenspaces are created independently in CS and in the orthogonal complement CS/sup /spl perp//. Some refinements of the two eigenspaces are also discussed. By combining the two eigenspaces, we can easily realize face identification that is robust to illumination change, even when too few images are registered. Through experiments, we show the effectiveness of the decomposed eigenfaces as compared with conventional methods.
eigenvalues and eigenfunctions
principal component analysis
Digital Object Identifier: 10.1109/CVPR.2001.990575
Published with permission from the copyright holder. This is the institute's copy, as published in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, 2001, Vol. 1, Pages I864-I871.
Copyright © 2001 IEEE. All rights reserved.
Computer Vision and Pattern Recognition