Finger vein recognition using LBP variance with global matching

Finger vein recognition has been identified as a stable biometrics technique that has many advantages as compared to other techniques. The biggest challenge that is faced while using this technique is to make the features rotarionally invariant. In this paper, local binary pattern variance (LBPV) is proposed to address this challenge and to characterize the local contrast information into the one-dimensional LBP histogram. Global matching method is used to further quicken the matching scheme and to reduce feature dimensions using distance measurement resulting to minimal computational cost. The classification rate of this method is tested using support vector machine (SVM), which gives it a high classification rate.