# Secure Face Matching Using Fully Homomorphic Encryption

arXiv: https://arxiv.org/abs/1805.00577 · HTML: https://mlsanity.com/paper/1805.00577/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 29 of 29)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters](https://mlsanity.com/paper/2201.12467/) (2022)
- [Towards Protecting Face Embeddings in Mobile Face Verification Scenarios](https://mlsanity.com/paper/2110.00434/) (2021)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [IronMask: Modular Architecture for Protecting Deep Face Template](https://mlsanity.com/paper/2104.02239/) (2021)
- [Minutiae-Guided Fingerprint Embeddings via Vision Transformers](https://mlsanity.com/paper/2210.13994/) (2022)
- [SlerpFace: Face Template Protection via Spherical Linear Interpolation](https://mlsanity.com/paper/2407.03043/) (2024)
- [Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection](https://mlsanity.com/paper/2107.12675/) (2021)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)
- [Enhancing Privacy in Face Analytics Using Fully Homomorphic Encryption](https://mlsanity.com/paper/2404.16255/) (2024)
- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Towards Privacy-Preserving, Real-Time and Lossless Feature Matching](https://mlsanity.com/paper/2208.00214/) (2022)
- [Blind-Match: Efficient Homomorphic Encryption-Based 1:N Matching for Privacy-Preserving Biometric Identification](https://mlsanity.com/paper/2408.06167/) (2024)
- [Securing Face and Fingerprint Templates in Humanitarian Biometric Systems](https://mlsanity.com/paper/2508.18415/) (2025)
- [CryptoFace: End-to-End Encrypted Face Recognition](https://mlsanity.com/paper/2509.00332/) (2025)
- [FaRO 2: an Open Source, Configurable Smart City Framework for Real-Time Distributed Vision and Biometric Systems](https://mlsanity.com/paper/2209.12962/) (2022)
- [Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition](https://mlsanity.com/paper/2404.02696/) (2024)
- [CipherFace: A Fully Homomorphic Encryption-Driven Framework for Secure Cloud-Based Facial Recognition](https://mlsanity.com/paper/2502.18514/) (2025)
- [AMB-FHE: Adaptive Multi-biometric Fusion with Fully Homomorphic Encryption](https://mlsanity.com/paper/2503.23949/) (2025)
- [A Deeper Dive into the Irreversibility of PolyProtect: Making Protected Face Templates Harder to Invert](https://mlsanity.com/paper/2605.03857/) (2026)
- [ArctyrEX : Accelerated Encrypted Execution of General-Purpose Applications](https://mlsanity.com/paper/2306.11006/) (2023)
- [Adaptive Hybrid Masking Strategy for Privacy-Preserving Face Recognition Against Model Inversion Attack](https://mlsanity.com/paper/2403.10558/) (2024)
- [FaceCloak: Learning to Protect Face Templates](https://mlsanity.com/paper/2504.06131/) (2025)
- [Privacy-preserving Preselection for Face Identification Based on Packing](https://mlsanity.com/paper/2507.02414/) (2025)
- [Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems](https://mlsanity.com/paper/2508.11419/) (2025)
- [Secure and Scalable Face Retrieval via Cancelable Product Quantization](https://mlsanity.com/paper/2509.00781/) (2025)
- [Scalable Secure Biometric Authentication without Auxiliary Identifiers](https://mlsanity.com/paper/2604.25071/) (2026)
- [Is it Possible to Generate Irreversible PolyProtected Templates from Face Embeddings using System-Specific Keys?](https://mlsanity.com/paper/2610.01385/) (2026)

## Abstract (arXiv)

Face recognition technology has demonstrated tremendous progress over the past few years, primarily due to advances in representation learning. As we witness the widespread adoption of these systems, it is imperative to consider the security of face representations. In this paper, we explore the practicality of using a fully homomorphic encryption based framework to secure a database of face templates. This framework is designed to preserve the privacy of users and prevent information leakage from the templates, while maintaining their utility through template matching directly in the encrypted domain. Additionally, we also explore a batching and dimensionality reduction scheme to trade-off face matching accuracy and computational complexity. Experiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that secure face matching can be practically feasible (16 KB template size and 0.01 sec per match pair for 512-dimensional features from SphereFace) while exhibiting minimal loss in matching performance.