# Multibiometric Secure System Based on Deep Learning

arXiv: https://arxiv.org/abs/1708.02314 · HTML: https://mlsanity.com/paper/1708.02314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2017.

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

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [Confidence-aware Contrastive Learning for Selective Classification](https://mlsanity.com/paper/2406.04745/) (2024)
- [HEFT: Homomorphically Encrypted Fusion of Biometric Templates](https://mlsanity.com/paper/2208.07241/) (2022)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [Detecting Face Synthesis Using a Concealed Fusion Model](https://mlsanity.com/paper/2401.04257/) (2024)

## Abstract (arXiv)

In this paper, we propose a secure multibiometric system that uses deep neural networks and error-correction coding. We present a feature-level fusion framework to generate a secure multibiometric template from each user's multiple biometrics. Two fusion architectures, fully connected architecture and bilinear architecture, are implemented to develop a robust multibiometric shared representation. The shared representation is used to generate a cancelable biometric template that involves the selection of a different set of reliable and discriminative features for each user. This cancelable template is a binary vector and is passed through an appropriate error-correcting decoder to find a closest codeword and this codeword is hashed to generate the final secure template. The efficacy of the proposed approach is shown using a multimodal database where we achieve state-of-the-art matching performance, along with cancelability and security.