Abstract
The increased need for unattended authentication in multiple scenarios has motivated a wide deployment of biometric systems in the last few years. This has in turn led to the disclosure of security concerns specifically related to biometric systems. Among them, Presentation Attacks (PAs, i.e., attempts to log into the system with a fake biometric characteristic or presentation attack instrument) pose a severe threat to the security of the system: any person could eventually fabricate or order a gummy finger or face mask to impersonate someone else. The biometrics community has thus made a considerable effort to the development of automatic Presentation Attack Detection (PAD) mechanisms, for instance through the international LivDet competitions. In this context, we present a novel fingerprint PAD scheme based on $i)$ a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and $ii)$ an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features. The approach is evaluated on a database comprising over 4700 samples, stemming from 562 different subjects and 35 different presentation attack instrument (PAI) species. The results show the soundness of the proposed approach with a detection equal error rate (D-EER) as low as 1.36\% even in a realistic scenario where five different PAI species are considered only for testing purposes (i.e., unknown attacks).
Highlights
T HERE is an increasing demand in the current society for automatic and reliable authentication of individuals in a wide number of scenarios
We present a novel fingerprint presentation attack detection (PAD) scheme based on i ) a new capture device able to acquire images within the short wave infrared (SWIR) spectrum, and ii ) an in-depth analysis of several state-of-the-art techniques based on both handcrafted and deep learning features
We include the main definitions stated within the ISO/IEC 30107-3 standard on biometric presentation attack detection - part 3: testing and reporting [56], which will be used throughout the article: Bona fide presentation: “interaction of the biometric capture subject and the biometric data capture subsystem in the fashion intended by the policy of the biometric system”
Summary
T HERE is an increasing demand in the current society for automatic and reliable authentication of individuals in a wide number of scenarios. Novel PAI species were considered in [32], noting that the error rates were multiplied by a factor of six when unknown PAI species were tested, with respect to the detection accuracy reached on known attacks This challenging scenario has been studied in other biometric characteristics such as face [37] and iris [38]. A preliminary DL approach based on a pre-trained CNN model was tested on the same database in [52], achieving perfect detection rates over the small preliminary database Keeping these thoughts in mind, the main contributions of this work compared with the state-of-the-art can be summarised as follows:.
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