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 [BibTeX] [Marc21]
Indexing Protected Deep Face Templates by Frequent Binary Patterns
Type of publication: Conference paper
Citation: Osorio-Roig_IJCB_2022
Publication status: Published
Booktitle: Proceedings of the 2022 International Joint Conference on Biometrics (IJCB)
Year: 2022
Publisher: IEEE
Location: Abu Dhabi, United Arab Emirates (UAE)
URL: https://ieeexplore.ieee.org/ab...
DOI: 10.1109/IJCB54206.2022.10007939
Abstract: In this work, we present a simple biometric indexing scheme which is binning and retrieving cancelable deep face templates based on frequent binary patterns. The simplicity of the proposed approach makes it applicable to unprotected as well as protected, i.e. cancelable, deep face templates. As such, this approach represents to the best of the authors' knowledge the first generic indexing scheme that can be applied to arbitrary cancelable face templates (o binary representation). In experiments, deep face templates are obtained from the Labelled Faces in the Wild (LFW) dataset using the ArcFace face recognition system for feature extraction. Protected templates are then generated by employing different cancelable biometric schemes, i.e. BioHashing and two variants of Index-of-Maximum Hashing. The proposed indexing scheme is evaluated on closed- and open-set identification scenarios. It is shown to maintain the recognition accuracy of the baseline system while reducing the penetration rate and hence the workload of identifications to approximately 40%.
Authors Osorio-Roig, Dailé
Rathgeb, Christian
Otroshi Shahreza, Hatef
Busch, Christoph
Marcel, SĂ©bastien
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  • Osorio-Roig_IJCB_2022.pdf