# Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection

arXiv: https://arxiv.org/abs/2107.12675 · HTML: https://mlsanity.com/paper/2107.12675/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 136 references that are in the map.
- First posted on arXiv 27 Jul 2021.

## Builds on (most cited first, 11 of 11)

- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [On the Intrinsic Dimensionality of Image Representations](https://mlsanity.com/paper/1803.09672/) (2018)
- [Secure Face Matching Using Fully Homomorphic Encryption](https://mlsanity.com/paper/1805.00577/) (2018)
- [HERS: Homomorphically Encrypted Representation Search](https://mlsanity.com/paper/2003.12197/) (2020)
- [A Comprehensive Overview of Biometric Fusion](https://mlsanity.com/paper/1902.02919/) (2019)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [A Cryptanalysis of Two Cancelable Biometric Schemes based on Index-of-Max Hashing](https://mlsanity.com/paper/1910.01389/) (2019)
- [Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification](https://mlsanity.com/paper/1711.01587/) (2017)
- [Cancelable Indexing Based on Low-rank Approximation of Correlation-invariant Random Filtering for Fast and Secure Biometric Identification](https://mlsanity.com/paper/1804.01670/) (2018)
- [Signal-level Fusion for Indexing and Retrieval of Facial Biometric Data](https://mlsanity.com/paper/2103.03692/) (2021)

## Built on by (most cited first, 5 of 5)

- [Biometric Template Protection for Neural-Network-based Face Recognition Systems: A Survey of Methods and Evaluation Techniques](https://mlsanity.com/paper/2110.05044/) (2021)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)
- [IDFace: Face Template Protection for Efficient and Secure Identification](https://mlsanity.com/paper/2507.12050/) (2025)
- [Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns](https://mlsanity.com/paper/2310.03091/) (2023)
- [Privacy-preserving Preselection for Face Identification Based on Packing](https://mlsanity.com/paper/2507.02414/) (2025)

## Abstract (arXiv)

Computationally efficient, accurate, and privacy-preserving data storage and retrieval are among the key challenges faced by practical deployments of biometric identification systems worldwide. In this work, a method of protected indexing of biometric data is presented. By utilising feature-level fusion of intelligently paired templates, a multi-stage search structure is created. During retrieval, the list of potential candidate identities is successively pre-filtered, thereby reducing the number of template comparisons necessary for a biometric identification transaction. Protection of the biometric probe templates, as well as the stored reference templates and the created index is carried out using homomorphic encryption. The proposed method is extensively evaluated in closed-set and open-set identification scenarios on publicly available databases using two state-of-the-art open-source face recognition systems. With respect to a typical baseline algorithm utilising an exhaustive search-based retrieval algorithm, the proposed method enables a reduction of the computational workload associated with a biometric identification transaction by 90%, while simultaneously suffering no degradation of the biometric performance. Furthermore, by facilitating a seamless integration of template protection with open-source homomorphic encryption libraries, the proposed method guarantees unlinkability, irreversibility, and renewability of the protected biometric data.