Abstract
Within the last few decades, the need for subject authentication has grown steadily, and biometric recognition technology has been established as a reliable alternative to passwords and tokens, offering automatic decisions. However, as unsupervised processes, biometric systems are vulnerable to presentation attacks targeting the capture devices, where presentation attack instruments (PAI) instead of bona fide characteristics are presented. Due to the capture devices being exposed to the public, any person could potentially execute such attacks. In this work, a fingerprint capture device based on thin film transistor (TFT) technology has been modified to additionally acquire the impedances of the presented fingers. Since the conductance of human skin differs from artificial PAIs, those impedance values were used to train a presentation attack detection (PAD) algorithm. Based on a dataset comprising 42 different PAI species, the results showed remarkable performance in detecting most attack presentations with an APCER = 2.89% in a user-friendly scenario specified by a BPCER = 0.2%. However, additional experiments utilising unknown attacks revealed a weakness towards particular PAI species.
Highlights
Within the last decade, biometric recognition systems have been deployed in several applications used in our daily lives
A secure biometric system requires an automated presentation attack detection (PAD) module, which needs to learn the differences between bona fide presentations (BPs) and attack presentations (APs) [4]
We describe the functionality of the capture device which was used to acquire a dataset of 757 BPs and 915 APs from 42 different presentation attack instruments (PAI) species
Summary
Biometric recognition systems have been deployed in several applications used in our daily lives. The intention of the attacker can be either to impersonate someone’s identity or to conceal his own identity In both cases, a presentation attack instrument (PAI) instead of the bona fide characteristic is presented to the capture device. A secure biometric system requires an automated presentation attack detection (PAD) module, which needs to learn the differences between bona fide presentations (BPs) and attack presentations (APs) [4]. PAD algorithms on identical datasets since 2009 These efforts allowed significant research and development of new countermeasures across different biometric characteristics. The development of fingerprint PAD methods requires a dataset with BPs and APs. As a consequence, PAIs need to be created from either cooperative target subjects or latent (or synthetic) fingerprints.
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