Enhancement of Finger Vein Total Variation Decomposition by Using PAD Method

As an emerging biometric modality, finger vein recognition has received considerable attentions. However, recent studies have shown that finger vein biometrics is vulnerable to presentation attacks, i.e. printed versions of authorized individuals’ finger veins could be used to gain access to facilities or services. In this paper, we have designed a specific for finger vein presentation attack detection (PAD). First, we use total variation (TV) regularization to decompose original finger vein images into structure and noise components, which represent the degrees of blurriness and the noise distribution. Second, a block local binary pattern (LBP) descriptor is used to encode both structure and noise information in the decomposed components. Finally, we use a cascaded support vector machine (SVM) model for classification, by which finger vein presentation attacks can be effectively detected. To evaluate the performance of our approach, we constructed a new finger vein presentation attack database. Extensive experimental results gleaned from the two finger vein presentation attack databases and a palm vein presentation attack database show that our method clearly outperforms stateof-the-art methods.