Neural Fingerprint Enhancement

Abstract

Biometrics fingerprint matching has been done with a heavily hand-tuned and designed process of classical computer vision techniques for several decades. This approach has lead to accurate solutions solving crimes today, and as such little effort has been placed on using deep learning in this domain. Given that convolutional neural networks have shown dominance for most other image based problems, we re-evaluate their potential for improving the fingerprint process. By leveraging synthetic data generators we show that one can train a neural fingerprint enhancer that improves matching accuracy on real fingerprint images. Our approach is both simple in design and for potential deployment and adoption in real world use.