C
Christian Rathgeb
Researcher at Darmstadt University of Applied Sciences
Publications - 230
Citations - 5215
Christian Rathgeb is an academic researcher from Darmstadt University of Applied Sciences. The author has contributed to research in topics: Biometrics & Iris recognition. The author has an hindex of 33, co-authored 188 publications receiving 3837 citations. Previous affiliations of Christian Rathgeb include University of Salzburg.
Papers
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Journal ArticleDOI
A survey on biometric cryptosystems and cancelable biometrics
Christian Rathgeb,Andreas Uhl +1 more
TL;DR: A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented and state-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.
Journal ArticleDOI
Face Recognition Systems Under Morphing Attacks: A Survey
TL;DR: A conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications, and technical considerations and tradeoffs of the surveyed methods are discussed.
Journal ArticleDOI
General Framework to Evaluate Unlinkability in Biometric Template Protection Systems
TL;DR: This paper proposes a new general framework for the evaluation of biometric templates’ unlinkability and applies it to assess the un linkability of the four state-of-the-art techniques for biometric template protection: biometric salting, bloom filters, homomorphic encryption, and block re-mapping.
Journal ArticleDOI
Demographic Bias in Biometrics: A Survey on an Emerging Challenge
TL;DR: The main contributions of this article are an overview of the topic of algorithmic bias in the context of biometrics, a comprehensive survey of the existing literature on biometric bias estimation and mitigation, and a discussion of the pertinent technical and social matters.
Proceedings ArticleDOI
Alignment-free cancelable iris biometric templates based on adaptive bloom filters
TL;DR: In the presented work alignment-free cancelable iris biometrics based on adaptive Bloom filters are proposed and it is demonstrated that the proposed system maintains biometric performance for diverse iris recognition algorithms, protecting biometric templates at high security levels.