The invention discloses a 3D palmprint identification method. In the method, only the local features of blocks of a 3D palmprint, namely, a phase and
surface type block
histogram, are adopted. A 3D palmprint
feature model is proposed. The identification effect is improved, and moreover, the use of a 2D palmprint image is avoided so that the whole
system is free from the influence of
light intensity and
scratch marks. The local features of the 3D palmprint adopted are robust to small translation, rotation and even zooming of images, so that there is no need to use multiple translation, nearestiteration, cross-correlation and other methods for
image alignment, and the identification efficiency is improved. An intermediate term is added in a
sparse representation classifier, and an improvedsparse representation classifier is proposed by improving the
sparse coefficient. The
sparse coefficient can be calculated before 3D palmprint classification, and a subspace method is used to compareimages in the classification process. Thus, when there are a lot of samples in the training
library, there is no need to compare testing samples and training samples one by one. The
data redundancy, computation and
processing time are reduced.