Finger-Vein Authentication Based on Wide Line Detector and Pattern Normalization

In the finger-vein authentication, there are two problems in practice. One is that the quality of the vein image will be reduced under bad environment conditions; the other is the irregular distortion of the image caused by the variance of the finger poses. Both problems raise the error ratios. In this paper, we introduced a wide line detector for feature extraction, which can obtain precise width information of the vein and increase the information of the extracted feature from low quality image. We also developed a new pattern normalization model based on a hypothesis that the finger’s cross-sections are approximately ellipses and the vein that can be imaged is close to the finger surface. It can effectively reduce the distortion caused by the pose. In our experiment based on a database containing 50,700 images, our method shows advantages on dealing with the low quality data collected from the practical personal authentication system.