Although it has received increasing researchers’ attention in recent years, palm-vein recognition still 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 chapter proposes an end-to-end generative adversarial network to automatically extract the vein pattern network, thus without resorting to any hand-crafted segmentation techniques of grayscale images into vein pixels and background. Firstly, we label the palm-vein pixels based on a combination of handcrafted 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 an image 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 and PolyU palm-vein datasets demonstrate the effectiveness of our proposed method.