Abstract To carry out the finger vein recognition quickly and effectively, an algorithm of finger vein recognition is proposed according to the characteristics of bidirection two dimensional principal component analysis ((2D)2PCA) reducing the dimensions. The algorithm is bidirection weighted (2D)2PCA with eigenvalue normalization one ((OW2D)2PCA) based on preprocessing image of the figure vein image. The effect of the rate of cumulate eigenvalue on (2D)2PCA is analyzed, and the effect of the weighted value, the weighted value with eigenvalue normalization one and the rate of cumulate eigenvalue on W(2D)2PCA、OW(2D)2PCA、(W2D)2PCA and (OW2D)2PCA are analyzed as well. Experimental results on our database of finger vein images show that the presented method achieves high recognition accuracy. The redundant information of eigenvectors extracted by (2D)2PCA is restrained strongly, and the bi direction weighted effect is better than the one direction weighted effect. The average recognition rate of (OW2D)2PCA is higher than those of 2DPCA、(2D)2PCA、W(2D)2PCA、(W2D)2PCA and OW(2D)2PCA.
GUAN Feng-Xu,WANG Ke-Jun,LIU Jing-Yu等. Bi Direction Weighted (2D)2 PCA with Eigenvalue Normalization One for Finger Vein Recognition[J]. , 2011, 24(3): 417-424.
2011, Vol. 24 
