# Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems

arXiv: https://arxiv.org/abs/2508.11419 · HTML: https://mlsanity.com/paper/2508.11419/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 10 of 10)

- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [IDiff-Face: Synthetic-based Face Recognition through Fizzy Identity-Conditioned Diffusion Models](https://mlsanity.com/paper/2308.04995/) (2023)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [An Overview of Privacy-enhancing Technologies in Biometric Recognition](https://mlsanity.com/paper/2206.10465/) (2022)
- [Deep Hashing for Secure Multimodal Biometrics](https://mlsanity.com/paper/2012.14758/) (2020)
- [Benchmarking fixed-length Fingerprint Representations across different Embedding Sizes and Sensor Types](https://mlsanity.com/paper/2307.08615/) (2023)
- [IHashNet: Iris Hashing Network based on efficient multi-index hashing](https://mlsanity.com/paper/2012.03881/) (2020)
- [Understanding Biometric Entropy and Iris Capacity: Avoiding Identity Collisions on National Scales](https://mlsanity.com/paper/2308.03189/) (2023)
- [AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption](https://mlsanity.com/paper/2503.23949/) (2025)

## Abstract (arXiv)

Biometric recognition is widely used, making the privacy and security of extracted templates a critical concern. Biometric Template Protection schemes, especially those utilizing Homomorphic Encryption, introduce significant computational challenges due to increased workload. Recent advances in deep neural networks have enabled state-of-the-art feature extraction for face, fingerprint, and iris modalities. The ubiquity and affordability of biometric sensors further facilitate multi-modal fusion, which can enhance security by combining features from different modalities. This work investigates the biometric performance of reduced multi-biometric template sizes. Experiments are conducted on an in-house virtual multi-biometric database, derived from DNN-extracted features for face, fingerprint, and iris, using the FRGC, MCYT, and CASIA databases. The evaluated approaches are (i) explainable and straightforward to implement under encryption, (ii) training-free, and (iii) capable of generalization. Dimensionality reduction of feature vectors leads to fewer operations in the Homomorphic Encryption (HE) domain, enabling more efficient encrypted processing while maintaining biometric accuracy and security at a level equivalent to or exceeding single-biometric recognition. Our results demonstrate that, by fusing feature vectors from multiple modalities, template size can be reduced by 67 % with no loss in Equal Error Rate (EER) compared to the best-performing single modality.