Secure Triplet Loss: Achieving Cancelability and Non-Linkability in End-to-End Deep Biometrics

نویسندگان

چکیده

Biometric systems store sensitive personal data that need to be highly protected. However, state-of-the-art template protection schemes generally consist of separate processes, inspired by salting, hashing, or encryption, limit the achievable performance. Moreover, these are inadequate protect current biometric models as they rely on end-to-end deep learning methods. After proposing Secure Triplet Loss, focused cancelability, we now reformulate it address problem linkability. Evaluated verification with off-the-person electrocardiogram (ECG) and unconstrained face images, proposed method proves successful in training secure from scratch adapting a pretrained model make secure. The results show this new formulation Loss succeeds optimizing verify non-linkability, non-invertibility.

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ژورنال

عنوان ژورنال: IEEE transactions on biometrics, behavior, and identity science

سال: 2021

ISSN: ['2637-6407']

DOI: https://doi.org/10.1109/tbiom.2020.3046620