Palmprint Recognition Based on Multilinear Independent Component Analysis

In palmprint recognition,in order to learn the higher order dependencies associated with the different factors quickly and effectively, this paper uses Multilinear Independent Component Analysis(MICA) to the original tensor objects to obtain the low-dimension mode matrix.The palmprint images are projected onto the mode matrix for extracting the core tensors.Palmprint matching is implemented by calculating the cosine distance between two core tensors.Experiment based on PolyU plmprint database shows that compared with Principal Component Analysis(PCA), 2DPCA,Independent Component Analysis(ICA) and Multilinear Principal Component Analysis(MPCA),recognition rate of the new algorithm is the highest,and it meets real-time requirements of the system.