Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures

TL;DR

This paper explores the use of time-series and color-sensing fingerprint captures to enhance presentation attack detection, demonstrating that combining static and dynamic features improves spoofing detection accuracy.

Contribution

It introduces a new dataset with over 36,000 sequences and evaluates two PAD methods, showing that fusing static and dynamic features enhances detection performance.

Findings

Fusion of static and dynamic features improves PAD accuracy.

Color and time-series data provide complementary information.

Proposed methods outperform static-only approaches.

Abstract

Fingerprint capture systems can be fooled by widely accessible methods to spoof the system using fake fingers, known as presentation attacks. As biometric recognition systems become more extensively relied upon at international borders and in consumer electronics, presentation attacks are becoming an increasingly serious issue. A robust solution is needed that can handle the increased variability and complexity of spoofing techniques. This paper demonstrates the viability of utilizing a sensor with time-series and color-sensing capabilities to improve the robustness of a traditional fingerprint sensor and introduces a comprehensive fingerprint dataset with over 36,000 image sequences and a state-of-the-art set of spoofing techniques. The specific sensor used in this research captures a traditional gray-scale static capture and a time-series color capture simultaneously. Two different methods for Presentation Attack Detection (PAD) are used to assess the benefit of a color dynamic capture. The first algorithm utilizes Static-Temporal Feature Engineering on the fingerprint capture to generate a classification decision. The second generates its classification decision using features extracted by way of the Inception V3 CNN trained on ImageNet. Classification performance is evaluated using features extracted exclusively from the static capture, exclusively from the dynamic capture, and on a fusion of the two feature sets. With both PAD approaches we find that the fusion of the dynamic and static feature-set is shown to improve performance to a level not individually achievable.