Comparative analysis of segmentation and generative models for fingerprint retrieval task

TL;DR

This paper compares generative and segmentation deep learning models for fingerprint image enhancement, creating a new dataset, and finds that U-net outperforms GAN-based models in noise removal and image reconstruction.

Contribution

It introduces the NFD dataset for training fingerprint enhancement models and provides a comparative analysis of GAN and segmentation approaches.

Findings

U-net outperforms GAN models in fingerprint noise removal

Created the NFD dataset with realistic noisy fingerprint images

Quantitative and qualitative analysis supports U-net's superior performance

Abstract

Biometric Authentication like Fingerprints has become an integral part of the modern technology for authentication and verification of users. It is pervasive in more ways than most of us are aware of. However, these fingerprint images deteriorate in quality if the fingers are dirty, wet, injured or when sensors malfunction. Therefore, extricating the original fingerprint by removing the noise and inpainting it to restructure the image is crucial for its authentication. Hence, this paper proposes a deep learning approach to address these issues using Generative (GAN) and Segmentation models. Qualitative and Quantitative comparison has been done between pix2pixGAN and cycleGAN (generative models) as well as U-net (segmentation model). To train the model, we created our own dataset NFD - Noisy Fingerprint Dataset meticulously with different backgrounds along with scratches in some images to make it more realistic and robust. In our research, the u-net model performed better than the GAN networks