Finger Vein Recognition Based on (2D)‎2 PCA and Metric Learning

Abstract EN

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers.

In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning.

It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals.

Besides, the SMOTE technology is adopted to solve the class-imbalance problem.

Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%.