Robust Minutiae Extractor: Integrating Deep Networks and Fingerprint Domain Knowledge

TL;DR

This paper introduces MinutiaeNet, a deep learning-based method that integrates fingerprint domain knowledge to automatically extract minutiae with high accuracy, improving fingerprint matching and evaluation.

Contribution

It presents a novel two-stage neural network framework that combines domain knowledge with deep learning for robust minutiae extraction from fingerprints.

Findings

Outperforms state-of-the-art in precision and recall on NIST SD27 and FVC 2004 datasets.

Effectively integrates fingerprint domain knowledge with deep neural networks.

Provides a scalable approach for training fingerprint matching algorithms.

Abstract

We propose a fully automatic minutiae extractor, called MinutiaeNet, based on deep neural networks with compact feature representation for fast comparison of minutiae sets. Specifically, first a network, called CoarseNet, estimates the minutiae score map and minutiae orientation based on convolutional neural network and fingerprint domain knowledge (enhanced image, orientation field, and segmentation map). Subsequently, another network, called FineNet, refines the candidate minutiae locations based on score map. We demonstrate the effectiveness of using the fingerprint domain knowledge together with the deep networks. Experimental results on both latent (NIST SD27) and plain (FVC 2004) public domain fingerprint datasets provide comprehensive empirical support for the merits of our method. Further, our method finds minutiae sets that are better in terms of precision and recall in comparison with state-of-the-art on these two datasets. Given the lack of annotated fingerprint datasets with minutiae ground truth, the proposed approach to robust minutiae detection will be useful to train network-based fingerprint matching algorithms as well as for evaluating fingerprint individuality at scale. MinutiaeNet is implemented in Tensorflow: https://github.com/luannd/MinutiaeNet