  Minutiae play a major role in fingerprint identification. Extracting reliable
minutiae is difficult for latent fingerprints which are usually of poor
quality. As the limitation of traditional handcrafted features, a fully
convolutional network (FCN) is utilized to learn features directly from data to
overcome complex background noises. Raw fingerprints are mapped to a
correspondingly-sized minutia-score map with a fixed stride. And thus a large
number of minutiae will be extracted through a given threshold. Then small
regions centering at these minutia points are entered into a convolutional
neural network (CNN) to reclassify these minutiae and calculate their
orientations. The CNN shares convolutional layers with the fully convolutional
network to speed up. 0.45 second is used on average to detect one fingerprint
on a GPU. On the NIST SD27 database, we achieve 53\% recall rate and 53\%
precise rate that outperform many other algorithms. Our trained model is also
visualized to show that we have successfully extracted features preserving
ridge information of a latent fingerprint.
