Dynamic Fingerprint Statistics: Application in Presentation Attack Detection

Abstract

Fingerprint recognition systems have proven significant performance in many services such as forensics, border control, and mobile applications. Even though fingerprint systems have shown high accuracy and user acceptance, concerns have raised questions about the possibility of having our fingerprint pattern stolen and presented to the system by an imposter. In this paper, we propose a dynamic presentation attack detection mechanism that seeks to mitigate presentation attacks. The adopted mechanism extracts the variation of global fingerprint features in video acquisition scenario and uses it to distinguish bona fide from attack presentations. For that purpose, a dynamic dataset has been collected from 11 independent subjects, 6 fingerprints per user, using thermal and optical sensors. A total of 792 bona fide presentations and 2772 attack presentations are collected. The final PAD subsystem is evaluated based on the standard ISO/IEC 30107-3. Considering SVM classification and 3 folds cross validation, the obtained error rates at 5% APCER are 18.1% BPCER for the thermal subset and 19.5% BPCER for the optical subset.

Fingerprint Presentation Attack Detection Utilizing Spatio-Temporal Features

“…The dynamic model was chosen because we have experimentally noticed that genuine fingerprint presentations demonstrate a unique development of the ridge/valley pattern due to natural phenomena such as elasticity and perspiration. Moreover, PAs have shown perceptual and statistical dynamic differences as shown in our previous work [ 5 ]. Thus, five state-of-the-art dynamic texture descriptors are selected to investigate fingerprint spatiotemporal features aiming to obtain high PAD classification accuracy.…”

“…To evaluate the performance of the proposed PAD subsystem, we use the dynamic dataset presented in [ 5 ]. In the initial stage of the experiment, a volume segmentation is applied to the database.…”

“…The database comprises 66 genuine fingerprints (thumb, index middle) taken from both hands of 11 independent subjects, and attacks using seven PAI species. A definite characterization of the protocol applied to produce this database is introduced in [ 5 ].…”

“…Finally, in our recent work [ 5 ], we investigated the dynamic statistics of fingerprints in different sensing technologies. Our study demonstrated that genuine fingerprints and attacks have statistical differences that can be used to classify attack and genuine presentations.…”

A Survey in Presentation Attack and Presentation Attack Detection

“…To evaluate the performance of the proposed PAD subsystem, we use the dynamic dataset presented in [233]. In the initial stage of the experiment, a volume segmentation is applied to the database.…”