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Auflistung nach Schlagwort "Face"

1 - 4 von 4
  • Konferenzbeitrag
    On the Application of Homomorphic Encryption to Face Identification
    (BIOSIG 2019 - Proceedings of the 18th International Conference of the Biometrics Special Interest Group, 2019) Drozdowski, Pawel; Buchmann, Nicolas; Rathgeb, Christian; Margraf, Marian; Busch, Christoph
    The data security and privacy of enrolled subjects is a critical requirement expected from biometric systems. This paper addresses said topic in facial biometric identification. In order to fulfil the properties of unlinkability, irreversibility, and renewability of the templates required for biometric template protection schemes, homomorphic encryption is utilised. In addition to achieving the aforementioned objectives, the use of homomorphic encryption ensures that the biometric performance remains completely unaffected by the template protection scheme. The main contributions of this paper are: It proposes an architecture of a system capable of performing biometric identification in the encrypted domain, as well as provides and evaluates an implementation using two existing homomorphic encryption schemes. Furthermore, it discusses the pertinent technical considerations and challenges in this context.
  • Konferenzbeitrag
    OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
    (BIOSIG 2022, 2022) Pedro C Neto, Tiago Gonçalves
    Morphing attacks are one of the many threats that are constantly affecting deep face recognition systems. It consists of selecting two faces from different individuals and fusing them into a final image that contains the identity information of both. In this work, we propose a novel regularisation term that takes into account the existent identity information in both and promotes the creation of two orthogonal latent vectors.We evaluate our proposed method (OrthoMAD) in five different types of morphing in the FRLL dataset and evaluate the performance of our model when trained on five distinct datasets. With a small ResNet-18 as the backbone, we achieve state-of-the-art results in the majority of the experiments, and competitive results in the others.
  • Konferenzbeitrag
    Privacy Evaluation Protocols for the Evaluation of Soft-Biometric Privacy-Enhancing Technologies
    (BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group, 2020) Terhörst, Philipp; Huber, Marco; Damer, Naser; Rot, Peter; Kirchbuchner, Florian; Struc, Vitomir; Kuijper, Arjan
    Biometric data includes privacy-sensitive information, such as soft-biometrics. Soft-biometric privacy enhancing technologies aim at limiting the possibility of deducing such information. Previous works proposed several solutions to this problem using several different evaluation processes, metrics, and attack scenarios. The absence of a standardized evaluation protocol makes a meaningful comparison of these solutions difficult. In this work, we propose privacy evaluation protocols (PEPs) for privacy-enhancing technologies (PETs) dealing with soft-biometric privacy. Our framework evaluates PETs in the most critical scenario of an attacker that knows and adapts to the systems privacy-mechanism. Moreover, our PEPs differentiate between PET of learning-based or training-free nature. To ensure that our protocol meets the highest standards in both cases, it is based on Kerckhoffs‘s principle of cryptography.
  • Konferenzbeitrag
    When Facial Recognition Systems become Presentation Attack Detectors
    (BIOSIG 2022, 2022) Lazaro Janier Gonzalez-Soler, Kevin Abadi Barhaugen
    Recently, biometric systems (BSs) have experienced a broad development mainly due to the great success of deep learning approaches. Generally, most BS provide high security and efficiency. However, they are still vulnerable to attack presentations (APs). To overcome such security issues, these schemes include a Presentation Attack Detection (PAD) module which determines whether the input sample stems from an AP or a bona fide presentation (BP). Traditionally, most PAD subsystems assess the biometric sample prior to the recognition module. In this work, we evaluate to what extent the inverted combination, where the biometric recognition module filters samples prior to the assessment of a PAD mechanism, leads to an overall PAD performance improvement. The experimental evaluation conducted over two well-known databases including challenging attacks, reports a significant improvement in the detection performance when input samples were first filtered by the biometric recognition: only 1% of the APs are accepted while at most 5% BPs are rejected by the PAD subsystem.