Multi-Modal Fingerprint Presentation Attack Detection: Evaluation On A New Dataset
This work addresses the problem of improving security in biometric systems for users and organizations, but it is incremental as it focuses on hardware-based enhancements rather than a fundamental breakthrough.
The paper tackled fingerprint presentation attack detection by evaluating multiple new sensing modalities, such as short-wave-infrared and near-infrared, showing their superiority over legacy methods in most cases with concrete comparisons to datasets like LivDet2015.
Fingerprint presentation attack detection is becoming an increasingly challenging problem due to the continuous advancement of attack preparation techniques, which generate realistic-looking fake fingerprint presentations. In this work, rather than relying on legacy fingerprint images, which are widely used in the community, we study the usefulness of multiple recently introduced sensing modalities. Our study covers front-illumination imaging using short-wave-infrared, near-infrared, and laser illumination; and back-illumination imaging using near-infrared light. Toward studying the effectiveness of each of these unconventional sensing modalities and their fusion for liveness detection, we conducted a comprehensive analysis using a fully convolutional deep neural network framework. Our evaluation compares different combination of the new sensing modalities to legacy data from one of our collections as well as the public LivDet2015 dataset, showing the superiority of the new sensing modalities in most cases. It also covers the cases of known and unknown attacks and the cases of intra-dataset and inter-dataset evaluations. Our results indicate that the power of our approach stems from the nature of the captured data rather than the employed classification framework, which justifies the extra cost for hardware-based (or hybrid) solutions. We plan to publicly release one of our dataset collections.