Minutiae extraction is an important step for robust fingerprint identification. However, existing minutia extraction algorithms rely on time consuming and fragile image enhancement steps in order to work robustly. We propose a new approach, combining enhancement and extraction into a Convolutional Neural Network (CNN). This network is trained from scratch using synthetic fingerprints. To bridge the gap between synthetic and real fingerprints, refinements are used. Here, an approach based on Generative Adversarial Networks (GANs) is used to generate fingerprints suited for training such a network and improving its matching score on real fingerprints.