Abstract:  In order to solve the shortcoming of fewer extracted line feature and lower recognition rates of
pathologic palmprint recognition algorithm, we proposed a recognition algorithm of pathologic palmprint based on
spectral fusion in non-subsampled shearlet domain. Firstly, the best spectral fused combination of multispectral
palmprint is selected. And it is decomposed to the multi-directions, multi-scales in non-subsampled shearlet
domain. Next, according to all levels characteristics of sub-bands images which had been decomposed, a new
fusion rule is designed to fuse the corresponding coefficient matrices. The fine lines feature of palmprint can be
obtained by the inverse transformation of the NSST(Nonsubsample Shearlet Transform) and the process of
mathematical morphology. Then the satisfactory closed circuits are searched by degree feature of pixels. Finally,
we proposed a method of combining the shape descriptors based on rectangle degree and eccentricity to recognize
closed pathologic palmprint. Experimental results show that this algorithm can extract rich feature of the
palmprint line, and can recognize six different types of closed pathologic palmprints accurately and the
recognition rate is more than 90%.

Key words: spectral fusion, palmprint, nonsubsample shearlet transform ( NSST ), closed circuit, pathologic palmprint