  Local Binary Patterns (LBP) are extensively used to analyze local texture
features of an image. Several new extensions to LBP-based texture descriptors
have been proposed, focusing on improving noise robustness by using different
coding or thresholding schemes. In this paper we propose three algorithms
(LBP), Shift Local Binary Pattern (SLBP), and Multi Shift Local Binary Pattern
(MSLBP),to extract features for palmprint images that help to obtain the best
unique and characteristic values of an image for identification. The Principal
Component Analysis (PCA) algorithm has been applied to reduce the size of the
extracted feature matrix in random space and in the matching process; the
Linear Discriminant Analysis (LDA) algorithm is used. Several experiments were
conducted on the large multispectral database (blue, green, red, and infrared)
of the University of Hong Kong. As result, distinguished and high results were
obtained where it was proved that, the blue spectrum is superior to all spectra
perfectly.
