Hand vein recognition based on multiple keypoints sets
Yiding Wang, Yun Fan, Weiping Liao, Kefeng Li, Lik‐Kwan Shark, Martin Roy Varley · 2012
Biometric authentication based on hand vein patterns has grown in popularity as a way to confirm personal identity. However, the imaging quality and variability of the vein images acquired by the near-infrared (NIR) device present challenges to achieve high classification accuracy. In this paper, a novel method for hand vein recognition by fusion of multiple sets of keypoints from the scale-invariant feature transform (SIFT) is proposed. While the use of SIFT enables classification to be unaffected by imaging quality and variability, the fusion reduces information redundancies and improves the discrimination power. The proposed method is tested on a database of 2040 images, and the experiment results show a good classification performance with a result of 97.95% recognition rate.