Fingerprint Presentation Attack Detection utilizing Time-Series, Color Fingerprint Captures
Richard Plesh, Keivan Bahmani, Ganghee Jang, David Yambay, Ken, Brownlee, Timothy Swyka, Peter Johnson, Arun Ross, Stephanie Schuckers

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…
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