Generalisable Presentation Attack Detection For Multiple Types Of Biometric Characteristics

  • Biometric systems have experienced a large development in recent years since they are accurate, secure, and in many cases, more user convenient than traditional credential-based access control systems. Inspite of their benefits, biometric systems are still vulnerable to attack presentations (APs), which can be easily launched by a fraudulentsubject without having a wide expert knowledge. This way, he/she can gain access to several applications, such as bank accounts and smartphone unlocking, where biometric systems are frequently deployed. In order to mitigate such threats and increase the security of biometric systems, the development of reliable Presentation Attack Detection (PAD) algorithms is of utmost importance to the research community.In the context of PAD, we explore in this Thesis different strategies and methods in order to improve the generalisation capability of PAD schemes. To that end, we propose the definition of a semantic common feature space which successfully discriminates bona fide presentations (BPs)1 from APs. In essence, this process is seeking for those significant features extracted from known PAI species samples that are observed in unknown PAI species. In addition, we explore several handcrafted techniques in order to build a reliable description of features per biometric characteristic studied. The experimental evaluation shows that a common feature space can be computed through the fusion between generative models and discriminative approaches. Remarkable detection performances for high-security thresholds lead to the construction of a convenient (i.e., low BP rejection rates or Bona fide Presentation Classification Error Rate (BPCER)) and secure (i.e., low AP acceptance rates or Attack Presentation Classification Error Rate (APCER)) PAD subsystem.

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Author:Lázaro Janier González-Soler
Publisher:Hochschule Darmstadt
Place of publication:Darmstadt
Referee:Ralf Dörner, Julian Fierrez, Martin Drahansky
Advisor:Christoph Busch, Andreas Heinemann, Marta Gomez-Barrero
Document Type:Doctoral Thesis
Year of Completion:2022
Publishing Institution:Hochschule Darmstadt
Granting Institution:Hochschule Darmstadt
Date of final exam:2022/12/14
Release Date:2023/02/07
Tag:Biometric Systems; generalisable feature spaces; presentation attac detection; semantic common feature spaces
Page Number:144
Institutes:Promotionszentrum Angewandte Informatik
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International