A Method of Data Augmentation to Train a Small Area Fingerprint Recognition Deep Neural Network with a Normal Fingerprint Database

TL;DR

This paper introduces a data augmentation technique that enables training small-area fingerprint recognition neural networks using standard fingerprint datasets, improving recognition accuracy for small-area fingerprints.

Contribution

The paper proposes a novel data augmentation method that allows training small-area fingerprint neural networks with existing large fingerprint datasets, addressing data scarcity issues.

Findings

Enhanced recognition accuracy for small-area fingerprints

Effective use of normal fingerprint datasets for training

Validated method through experimental tests

Abstract

Fingerprints are popular among the biometric based systems due to ease of acquisition, uniqueness and availability. Nowadays it is used in smart phone security, digital payment and digital locker. The traditional fingerprint matching methods based on minutiae are mainly applicable for large-area fingerprint and the accuracy rate would reduce significantly when dealing with small-area fingerprint from smart phone. There are many attempts to using deep learning for small-area fingerprint recognition, and there are many successes. But training deep neural network needs a lot of datasets for training. There is no well-known dataset for small-area, so we have to make datasets ourselves. In this paper, we propose a method of data augmentation to train a small-area fingerprint recognition deep neural network with a normal fingerprint database (such as FVC2002) and verify it via tests. The experimental results showed the efficiency of our method.