Abstract
In this paper, partial least square (PLS) regression is firstly employed in image processing. And a new technique coined partial least squares (PLS) regression, line-based PLS, is proposed for feature extraction of the images. To test this new approach, a series of experiments were performed on the famous face image database: ORL face database. Compared with newly proposed two dimensional principal component analysis (2DPCA), it can be found that the dimension of the feature vectors of the line-based PLS is no more than half of the 2DPCA’s while the recognition rate can retain at the same high level. Thus, the feature extraction based on line-based PLS regression is a feasible and effective method.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wan, YY., Du, JX., Li, K. (2006). A Novel Feature Extraction Approach to Face Recognition Based on Partial Least Squares Regression. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_131
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DOI: https://doi.org/10.1007/11816157_131
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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