Palm-vein recognition has received increasing researchers’ attention in recent years. However, palm-vein recognition faces various challenges in practical applications, one of which is the lack of robustness against image quality degradation, resulting in reduction of the verification accuracy. To address this problem, this paper proposes an end-to-end convolutional neural network to automatically extract vein network features, thus without resorting to any hand-crafted features. Firstly, we label the palm-vein pixels based on several handcraft-based segmentation methods and reconstruct a training set accordingly. Secondly, an end-to-end vein segmentation model is proposed based on a generative adversarial network. After training, this model outputs a map where each value is the probability that the corresponding pixel belongs to a vein pattern. The resulting map is then subject to binarization by thresholding and stored in a binary image, used subsequently for verification matching. The experimental results on the public CASIA palm-vein dataset demonstrate the effectiveness of our proposed method.