mlsanityData to 14 Oct 2026

A Deeper Dive into the Irreversibility of PolyProtect: Making Protected Face Templates Harder to Invert

arXiv 2605.03857 ↗First posted 5 May 2026cs.CV cs.CRcited by 1

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Abstract (arXiv)

This work presents a deeper analysis of the "irreversibility" property of PolyProtect, a biometric template protection method initially proposed for securing face embeddings. PolyProtect transforms embeddings into protected templates via multivariate polynomials, whose coefficients and exponents are distinct for each subject enrolled in the face recognition system. A polynomial is applied to consecutive sets of elements from a given embedding, where the amount of overlap between the sets is a tunable parameter. We begin our irreversibility analysis by demonstrating that PolyProtected templates are easier to invert using a numerical solver based on cosine distance, as opposed to Euclidean distance (used in the earlier PolyProtect work). To make this inversion more difficult, we then propose a "key selection algorithm", which tries to choose "keys" (coefficients and exponents of the PolyProtect polynomial) that enhance the irreversibility of PolyProtected templates, compared to when the keys are purely random. Our experiments show that this algorithm is effective at generating PolyProtected templates that are significantly more difficult to invert, and that it approximately equalises the irreversibility of PolyProtected templates generated using different "overlap" parameters. This allows for better control of the irreversibility versus accuracy trade-off, known to exist across different overlaps. We also show that accuracy in the PolyProtected domain can be affected by the range in which the embedding elements lie, but that this can be improved by normalizing the embeddings prior to applying PolyProtect. This work is reproducible using our open-source code.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 31 references that are in the map. First posted on arXiv 5 May 2026.

Builds on

15 of 31 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  3. 2017On the Reconstruction of Face Images from Deep Face Templatescited by 67
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  5. 2023EdgeFace: Efficient Face Recognition Model for Edge Devicescited by 33
  6. 2018Secure Face Matching Using Fully Homomorphic Encryptioncited by 29
  7. 2020HERS: Homomorphically Encrypted Representation Searchcited by 26
  8. 2020Beyond Identity: What Information Is Stored in Biometric Face Templates?cited by 23
  9. 2016Inverting face embeddings with convolutional neural networkscited by 22
  10. 2021Towards Protecting Face Embeddings in Mobile Face Verification Scenarioscited by 14
  11. 2021Deep Face Fuzzy Vault: Implementation and Performancecited by 12
  12. 2021Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniquescited by 11
3 more
  1. 2019Zero-Shot Deep Hashing and Neural Network Based Error Correction for Face Template Protectioncited by 9
  2. 2024in-Car Biometrics (iCarB) Datasets for Driver Recognition: Face, Fingerprint, and Voicecited by 2
  3. 2025Securing Face and Fingerprint Templates in Humanitarian Biometric Systemscited by 2

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