FIGO: Enhanced Fingerprint Identification Approach Using GAN and One Shot Learning Techniques

TL;DR

This paper introduces FIGO, a novel fingerprint identification system combining GAN-based enhancement and one-shot learning, significantly improving accuracy with minimal training data.

Contribution

The paper presents a new fingerprint identification approach integrating Pix2Pix for image enhancement and one-shot learning for feature extraction, outperforming traditional methods.

Findings

Pix2Pix model improves fingerprint image quality.

Enhanced system achieves higher recognition accuracy.

One-shot learning effectively differentiates fingerprints with minimal data.

Abstract

Fingerprint evidence plays an important role in a criminal investigation for the identification of individuals. Although various techniques have been proposed for fingerprint classification and feature extraction, automated fingerprint identification of fingerprints is still in its earliest stage. The performance of traditional \textit{Automatic Fingerprint Identification System} (AFIS) depends on the presence of valid minutiae points and still requires human expert assistance in feature extraction and identification stages. Based on this motivation, we propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques (FIGO). Our solution contains two components: fingerprint enhancement tier and fingerprint identification tier. First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier. With the proposed enhancement algorithm, the fingerprint identification model's performance is significantly improved. Furthermore, we develop another existing solution based on Gabor filters as a benchmark to compare with the proposed model by observing the fingerprint device's recognition accuracy. Experimental results show that our proposed Pix2pix model has better support than the baseline approach for fingerprint identification. Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process. Two twin convolutional neural networks (CNNs) with shared weights and parameters are used to obtain the feature vectors in this process. Using the proposed method, we demonstrate that it is possible to learn necessary information from only one training sample with high accuracy.