Bi-direction weighted (2D)2PCA with eigenvalue normalization one for finger vein recognition

作者：Guan, Feng Xu^*; Wang, Ke Jun; Liu, Jing Yu; Ma, Hui

来源：Pattem Recognition and Aitificial Intelligence, 2011, 24(3): 417-424.

• Bi-direction
• Finger vein
• Finger-vein recognition
• Pre-processing image
• Recognition accuracy
• Recognition rates
• Two dimensional principal component analysis
• Weighted values

To carry out the finger vein recognition quickly and effectively, an algorithm of finger vein recognition is proposed according to the characteristics of bi-direction two-dimensional principal component analysis ((2D)2PCA) reducing the dimensions. The algorithm is bi-direction 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.

• 出版日期2011
• 单位哈尔滨工程大学