Abstract
This paper presents a novel mechanism for fingerprint dynamic presentation attack detection. We utilize five spatio-temporal feature extractors to efficiently eliminate and mitigate different presentation attack species. The feature extractors are selected such that the fingerprint ridge/valley pattern is consolidated with the temporal variations within the pattern in fingerprint videos. An SVM classification scheme, with a second degree polynomial kernel, is used in our presentation attack detection subsystem to classify bona fide and attack presentations. The experiment protocol and evaluation are conducted following the ISO/IEC 30107-3:2017 standard. Our proposed approach demonstrates efficient capability of detecting presentation attacks with significantly low BPCER where BPCER is 1.11% for an optical sensor and 3.89% for a thermal sensor at 5% APCER for both.
Highlights
Fingerprint recognition is one of the oldest and most prevalent biometric modalities.It has shown attractive features such as high accuracy and user convenience; it has been applied in applications such as forensics, identity control, physical access control, and mobile devices
We assess the accuracy of the proposed Presentation Attack Detection (PAD) scheme and analyze the influence of selecting the feature extractor on the PAD subsystem efficiency
We present a novel fingerprint PAD approach in the dynamic scenario
Summary
Fingerprint recognition is one of the oldest and most prevalent biometric modalities. The generic biometric scheme is vulnerable at different points starting from the sensor to the recognition score/decision [2] Based on those vulnerabilities, biometric security is categorized in two main areas: (a) electronic security which concerns the digital process of the captured biometric trait (b) physical security which questions whether the biometric trait presentation is performed by a bona fide (i.e., genuine user) or by an attacker. The primary motivation for this study is that when expert attackers perform attacks, the 2-D impression of the attack presentation resembles the genuine fingerprint pattern, leading to a higher possibility that the attack will be classified as a bona fide presentation In this context, two recent investigations were carried out to support this claim.
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