# Benchmarking of Cancelable Biometrics for Deep Templates

arXiv: https://arxiv.org/abs/2302.13286 · HTML: https://mlsanity.com/paper/2302.13286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 26 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [On the Reconstruction of Face Images from Deep Face Templates](https://mlsanity.com/paper/1703.00832/) (2017)
- [Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing](https://mlsanity.com/paper/1703.05455/) (2017)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron](https://mlsanity.com/paper/2204.11054/) (2022)

## Built on by (most cited first, 2 of 2)

- [AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption](https://mlsanity.com/paper/2503.23949/) (2025)
- [FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing](https://mlsanity.com/paper/2508.05636/) (2025)

## Abstract (arXiv)

In this paper, we benchmark several cancelable biometrics (CB) schemes on different biometric characteristics. We consider BioHashing, Multi-Layer Perceptron (MLP) Hashing, Bloom Filters, and two schemes based on Index-of-Maximum (IoM) Hashing (i.e., IoM-URP and IoM-GRP). In addition to the mentioned CB schemes, we introduce a CB scheme (as a baseline) based on user-specific random transformations followed by binarization. We evaluate the unlinkability, irreversibility, and recognition performance (which are the required criteria by the ISO/IEC 24745 standard) of these CB schemes on deep learning based templates extracted from different physiological and behavioral biometric characteristics including face, voice, finger vein, and iris. In addition, we provide an open-source implementation of all the experiments presented to facilitate the reproducibility of our results.