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Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection.

Soha B Sandouka¹, Yakoub Bazi¹, Haikel Alhichri¹

¹Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.

Entropy (Basel, Switzerland)

|August 27, 2021

Summary

This summary is machine-generated.

This study enhances fingerprint presentation attack detection (PAD) by using a unified generative adversarial network (UGAN) to adapt models across different sensors. The novel approach significantly improves accuracy in cross-sensor and cross-material settings.

Keywords:

compound scaling network fingerprint liveness detection multitarget domain unified generative adversarial network (UGAN)

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Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography

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Published on: September 2, 2020

Area of Science:

Biometrics and Security
Computer Vision
Machine Learning

Background:

Fingerprint biometric systems face security vulnerabilities, necessitating improved presentation attack detection (PAD).
Existing PAD methods struggle with generalization across different sensors and materials.
Limited labeled data in target domains hinders model adaptation.

Purpose of the Study:

To develop a robust fingerprint PAD method that addresses limited sample issues in multiple target domains.
To enhance the generalization ability of PAD algorithms across diverse sensor and material settings.
To leverage knowledge transfer from a well-resourced source domain to under-resourced target domains.

Main Methods:

A unified generative adversarial network (UGAN) was trained for multi-domain image conversion, generating synthetic data to bridge domain gaps.
A scale-efficient network (EfficientNetV2) with multiple classifiers was trained on source and translated target domain data.
An additional fusion layer with learnable weights aggregated classifier outputs for improved PAD performance.

Main Results:

The proposed method demonstrated significant improvements in average classification accuracy on the LivDet2015 dataset.
Accuracy increased from 67.80% to 80.44% across twelve classification scenarios after adaptation.
The UGAN effectively reduced the distribution shift between fingerprint representations from different sensors.

Conclusions:

The proposed UGAN-based approach effectively enhances fingerprint PAD generalization across diverse domains.
Knowledge transfer from a source domain significantly improves performance in target domains with limited data.
This methodology offers a promising solution for building more secure and reliable fingerprint biometric systems.