  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.
