Sic-Gen: A Synthetic Iris-Code Generator

Drozdowski, Pawel; Rathgeb, Christian; Busch, Christoph

doi:10.23919/biosig.2017.8053520

Cited by 17 publications

(17 citation statements)

References 12 publications

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“…In contrast to most existing HE schemes, the proposed method supports the manipulation of real-valued biometric feature vectors without quantisation so that they can be packed into a single ciphertext. Drozdowski et al [ 65 ] proposed a system framework that allows face recognition in the cryptographic domain. This framework offers and assesses the implementation of HE schemes.…”

Section: He-based Approaches To Biometric Securitymentioning

confidence: 99%

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Yang

Wang

Cui

et al. 2023

Sensors

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The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.

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Section: He-based Approaches To Biometric Securitymentioning

confidence: 99%

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Yang

Wang

Cui

et al. 2023

Sensors

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“…workload reduction) and biometric performance. Scientific works on biometric cryptosystems for identification [35], [34], [38] have been commonly focused on providing evidence of practical applicability. The majority of them have contributed to reducing the effort at a one-to-one comparison level by feature transformation while other approaches [36], [39] worked on the reduction of one-to-many comparisons.…”

Section: Biometric Template Protectionmentioning

confidence: 99%

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Osorio-Roig

Rathgeb

Shahreza

et al. 2022

2022 IEEE International Joint Conference on Biometrics (IJCB)

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The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multibiometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-ofthe-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. The source code of the proposed multi-biometric indexing approach together with the composed multi-biometric dataset, will be made available to the research community once the article is accepted.

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“…In addition to the face, some research have also been conducted for other biometric modalities, e.g. fingerprint [14], [15], [16] and iris [17], [18].…”

Section: A Synthetic Data Generation For Face Analysismentioning

confidence: 99%

Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

Ibsen¹,

Rathgeb²,

Drozdowski³

et al. 2022

Preprint

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Systems that analyse faces have seen significant improvements in recent years and are today used in numerous application scenarios. However, these systems have been found to be negatively affected by facial alterations such as tattoos. To better understand and mitigate the effect of facial tattoos in facial analysis systems, large datasets of images of individuals with and without tattoos are needed. To this end, we propose a generator for automatically adding realistic tattoos to facial images. Moreover, we demonstrate the feasibility of the generation by training a deep learning-based model for removing tattoos from face images. The experimental results show that it is possible to remove facial tattoos from real images without degrading the quality of the image. Additionally, we show that it is possible to improve face recognition accuracy by using the proposed deep learning-based tattoo removal before extracting and comparing facial features.

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Sic-Gen: A Synthetic Iris-Code Generator

Cited by 17 publications

References 12 publications

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Face Beneath the Ink: Synthetic Data and Tattoo Removal with Application to Face Recognition

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