Finger vein verification based on a personalized best patches map

Finger vein pattern has become one of the most promising biometric identifiers. In this paper, we propose a robust finger vein verification method based on a personalized best patches map (PBPM). Firstly, some robust and discriminative visual words of finger vein are learned from traditional base feature such as local binary pattern (LBP). These visual words are named as finger vein textons (FVTs), which can well represent the visual primitives of finger vein. Secondly, we represent the finger vein image as a finger vein textons map (FVTM) by mapping each patch of the image into the closest FVT. Thirdly, by rejecting inconsistent patches, the PBPM of a certain individual is learned from these FVTMs which are extracted from the training samples of the same finger. Finally, the matched best patch ratio is used to measure similarity between the extracted FVTM of the input finger and the PBPM of a certain individual. Experimental results show that our method achieves satisfactory performance on the open PolyU database. In addition, it also has strong robustness and high accuracy on the self-built rotation and translation databases.