Altered Fingerprints: Detection and Localization

TL;DR

This paper presents a CNN-based method for detecting and localizing fingerprint alterations, and introduces a GAN to synthesize realistic altered fingerprints, significantly improving detection accuracy and addressing data scarcity.

Contribution

It introduces a novel CNN approach for detection/localization and a GAN for synthesizing altered fingerprints, enhancing research capabilities.

Findings

Achieved 99.24% true detection rate at 2% false detection rate.

Created a large dataset of 4,815 altered fingerprints for training and testing.

Generated synthetic altered fingerprints that resemble real ones.

Abstract

Fingerprint alteration, also referred to as obfuscation presentation attack, is to intentionally tamper or damage the real friction ridge patterns to avoid identification by an AFIS. This paper proposes a method for detection and localization of fingerprint alterations. Our main contributions are: (i) design and train CNN models on fingerprint images and minutiae-centered local patches in the image to detect and localize regions of fingerprint alterations, and (ii) train a Generative Adversarial Network (GAN) to synthesize altered fingerprints whose characteristics are similar to true altered fingerprints. A successfully trained GAN can alleviate the limited availability of altered fingerprint images for research. A database of 4,815 altered fingerprints from 270 subjects, and an equal number of rolled fingerprint images are used to train and test our models. The proposed approach achieves a True Detection Rate (TDR) of 99.24% at a False Detection Rate (FDR) of 2%, outperforming published results. The synthetically generated altered fingerprint dataset will be open-sourced.