# Signal-level Fusion for Indexing and Retrieval of Facial Biometric Data

arXiv: https://arxiv.org/abs/2103.03692 · HTML: https://mlsanity.com/paper/2103.03692/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 79 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [A Comprehensive Overview of Biometric Fusion](https://mlsanity.com/paper/1902.02919/) (2019)

## Built on by (most cited first, 1 of 1)

- [Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protection](https://mlsanity.com/paper/2107.12675/) (2021)

## Abstract (arXiv)

The growing scope, scale, and number of biometric deployments around the world emphasise the need for research into technologies facilitating efficient and reliable biometric identification queries. This work presents a method of indexing biometric databases, which relies on signal-level fusion of facial images (morphing) to create a multi-stage data-structure and retrieval protocol. By successively pre-filtering the list of potential candidate identities, the proposed method makes it possible to reduce the necessary number of biometric template comparisons to complete a biometric identification transaction. The proposed method is extensively evaluated on publicly available databases using open-source and commercial off-the-shelf recognition systems. The results show that using the proposed method, the computational workload can be reduced down to around 30%, while the biometric performance of a baseline exhaustive search-based retrieval is fully maintained, both in closed-set and open-set identification scenarios.