  Finger vein recognition has drawn increasing attention as one of the most
popular and promising biometrics due to its high distinguishes ability,
security and non-invasive procedure. The main idea of traditional schemes is to
directly extract features from finger vein images or patterns and then compare
features to find the best match. However, the features extracted from images
contain much redundant data, while the features extracted from patterns are
greatly influenced by image segmentation methods. To tack these problems, this
paper proposes a new finger vein recognition by generating code. The proposed
method does not require an image segmentation algorithm, is simple to calculate
and has a small amount of data. Firstly, the finger vein images were divided
into blocks to calculate the mean value. Then the centrosymmetric coding is
performed by using the generated eigenmatrix. The obtained codewords are
concatenated as the feature codewords of the image. The similarity between vein
codes is measured by the ratio of minimum Hamming distance to codeword length.
Extensive experiments on two public finger vein databases verify the
effectiveness of the proposed method. The results indicate that our method
outperforms the state-of-theart methods and has competitive potential in
performing the matching task.
