Laplacian bidirectional PCA for face recognition
نویسندگان
چکیده
Two-dimensional principal components analysis (2DPCA) needs more coefficients than principal components analysis (PCA) for image representation and hence needs more time for classification. The bidirectional PCA (BDPCA) is proposed to overcome these drawbacks of 2DPCA. Both 2DPCA and BDPCA, however, can work only in Euclidean space. In this paper, we propose Laplacian BDPCA (LBDPCA) representative face databases show that LBDPCA works well and it surpasses BDPCA. & 2010 Elsevier B.V. All rights reserved.
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ورودعنوان ژورنال:
- Neurocomputing
دوره 74 شماره
صفحات -
تاریخ انتشار 2010