Fingerprint minutiae extraction using deep learning

The high variability of fingerprint data (owing to, e.g., differences in quality, moisture conditions, and scanners) makes the task of minutiae extraction challenging, particularly when approached from a stance that relies on tunable algorithmic components, such as image enhancement. We pose minutiae extraction as a machine learning problem and propose a deep neural network — MENet, for Minutiae Extraction Network — to learn a data-driven representation of minutiae points. By using the existing capabilities of several minutiae extraction algorithms, we establish a voting scheme to construct training data, and so train MENet in an automated fashion on a large dataset for robustness and portability, thus eliminating the need for tedious manual data labelling. We present a post-processing procedure that determines precise minutiae locations from the output of MENet. We show that MENet performs favourably in comparisons against existing minutiae extractors.