Palmprint Recognition Based on Local Fisher Discriminant Analysis

Abstract

A new palmprint recognition method based on local Fisher discriminant analysis(LFDA) is proposed. In order to solve the singularity of the eigenvalue equation matrix in small-size-sample cases such as image recognition, image down-sample is first used to reduce the palmprint space dimensionality. The LFDA is applied to extract the low projection vectors. Then the training images and test images are projected onto the projection vectors to get the local palmprint feature vectors. Finally, the cosine distance between two feature vectors is calculated to match palmprint. The new algorithm is tested in PolyU plmprint database. The results show that compared with principal component analysis (PCA), Fisher discriminant analysis (FDA), independent component analysis (ICA), and kernel principal component analysis (KPCA), the recognition rate of the new algorithm is the highest which is 98.95%, and the recognition time is 0.031s, so it meets the real-time system specification.