Finger-vein recognition as a non-contact biometric technique has its inherent superiority on accuracy, speed, sanitation, maintenance and security. However, we found that due to posture changes when acquiring finger images, the discrepancy between different images from the same finger greatly lowers the performance of the entire system. In this paper, we define 6 types of finger posture changes, and analysis how they influence imaging. We then proposed a method to reconstruct a 3D normalized finger model from 2D images, which can be used to map finger area in 2D image into a new 2D coordinate system, thus being able to eliminate the influence of these six types of posture changes. We choose three kinds of feature extraction method, with a test data set from a practical finger-vein recognition system including 50,700 finger-vein images. The experimental results well proved the effectiveness of this method.