  Singular points detection is one of the most classical and important problem
in the field of fingerprint recognition. However, current detection rates of
singular points are still unsatisfactory, especially for low-quality
fingerprints. Compared with traditional image processing-based detection
methods, methods based on deep learning only need the original fingerprint
image but not the fingerprint orientation field. In this paper, different from
other detection methods based on deep learning, we treat singular points
detection as a semantic segmentation problem and just use few data for
training. Furthermore, we propose a new convolutional neural network called
SinNet to extract the singular regions of interest and then use a blob
detection method called SimpleBlobDetector to locate the singular points. The
experiments are carried out on the test dataset from SPD2010, and the proposed
method has much better performance than the other advanced methods in most
aspects. Compared with the state-of-art algorithms in SPD2010, our method
achieves an increase of 11% in the percentage of correctly detected
fingerprints and an increase of more than 18% in the core detection rate.
