Palmprint recognition based on multilinear principal component analysis and FLD

In order to decrease the effect of computational cost due to high dimensionality and extract the discriminant feature vectors which were propitious to classify,this paper proposed a new palmprint recognition method based on multilinear principal component analysis (MPCA) and FLD.First used the MPCA to operate directly on the original tensor objects.The low dimensional feature vectors were the input of FLD to extract the discriminant feature vectors.Calculated the cosine distance between two feature vectors to match palmprints.The experiment results of PolyU palmprint database show that compared with principal component analysis (PCA),PCA + FLD,2-dimension principal component analysis (2DPCA),independent component analysis (ICA),and MPCA,the recognition rate (RR) of the new algorithm is the highest which is 99.91%,and all the time for feature extraction and matching is 0.398 s,so it meets the real-time system specification.