Fingerprint Distortion Rectification using Deep Convolutional Neural Networks

TL;DR

This paper introduces a deep learning-based method to accurately and efficiently rectify elastic fingerprint distortions, significantly improving recognition performance especially in malicious distortion scenarios.

Contribution

It develops a DCNN model that estimates distortion parameters from fingerprint images, outperforming previous methods in accuracy and speed.

Findings

Significantly improves fingerprint matching accuracy on distorted samples.

Estimates distortion parameters ten times faster than previous dictionary search methods.

Effective in both normal and malicious distortion scenarios.

Abstract

Elastic distortion of fingerprints has a negative effect on the performance of fingerprint recognition systems. This negative effect brings inconvenience to users in authentication applications. However, in the negative recognition scenario where users may intentionally distort their fingerprints, this can be a serious problem since distortion will prevent recognition system from identifying malicious users. Current methods aimed at addressing this problem still have limitations. They are often not accurate because they estimate distortion parameters based on the ridge frequency map and orientation map of input samples, which are not reliable due to distortion. Secondly, they are not efficient and requiring significant computation time to rectify samples. In this paper, we develop a rectification model based on a Deep Convolutional Neural Network (DCNN) to accurately estimate distortion parameters from the input image. Using a comprehensive database of synthetic distorted samples, the DCNN learns to accurately estimate distortion bases ten times faster than the dictionary search methods used in the previous approaches. Evaluating the proposed method on public databases of distorted samples shows that it can significantly improve the matching performance of distorted samples.