Bi-directional weighted modular B2DPCA for finger vein recognition

Due to the restriction of acquisition equipment and other reasons, the finger vein line appeared broken, torsion, translation and other deformation in local area. Therefore, the traditional methods got low recognition accuracy. In this paper, combining the advantages of modular method and bi-directional weighted B2DPCA with eigenvalue normalization; the bidirectional two-dimensional principal component analysis (B2DPCA) algorithm is improved. The bi-directional weighted modular B2DPCA (BWMB2DPCA) algorithm is proposed in order to extract the local feature of finger vein effective. The nearest neighbor classifier was used to distinguish different fingers. Experimental results show that whether in the little training samples, or in the multiple training samples, this method can obtain a better recognition effect than 2DPCA, B2DPCA, WB2DPCA and MB2DPCA.