# Deep Hashing for Secure Multimodal Biometrics

arXiv: https://arxiv.org/abs/2012.14758 · HTML: https://mlsanity.com/paper/2012.14758/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion Recognition](https://mlsanity.com/paper/2307.15432/) (2023)
- [Deep Face Fuzzy Vault: Implementation and Performance](https://mlsanity.com/paper/2102.02458/) (2021)
- [DarkHash: A Data-Free Backdoor Attack Against Deep Hashing](https://mlsanity.com/paper/2510.08094/) (2025)
- [Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition](https://mlsanity.com/paper/2111.01325/) (2021)
- [Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval](https://mlsanity.com/paper/2310.14637/) (2023)
- [Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns](https://mlsanity.com/paper/2310.03091/) (2023)
- [Deep Learning in the Field of Biometric Template Protection: An Overview](https://mlsanity.com/paper/2303.02715/) (2023)
- [Multi-modal biometric authentication: Leveraging shared layer architectures for enhanced security](https://mlsanity.com/paper/2411.02112/) (2024)
- [Profile to Frontal Face Recognition in the Wild Using Coupled Conditional GAN](https://mlsanity.com/paper/2107.13742/) (2021)
- [Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers](https://mlsanity.com/paper/2108.02400/) (2021)
- [Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems](https://mlsanity.com/paper/2508.11419/) (2025)

## Abstract (arXiv)

When compared to unimodal systems, multimodal biometric systems have several advantages, including lower error rate, higher accuracy, and larger population coverage. However, multimodal systems have an increased demand for integrity and privacy because they must store multiple biometric traits associated with each user. In this paper, we present a deep learning framework for feature-level fusion that generates a secure multimodal template from each user's face and iris biometrics. We integrate a deep hashing (binarization) technique into the fusion architecture to generate a robust binary multimodal shared latent representation. Further, we employ a hybrid secure architecture by combining cancelable biometrics with secure sketch techniques and integrate it with a deep hashing framework, which makes it computationally prohibitive to forge a combination of multiple biometrics that pass the authentication. The efficacy of the proposed approach is shown using a multimodal database of face and iris and it is observed that the matching performance is improved due to the fusion of multiple biometrics. Furthermore, the proposed approach also provides cancelability and unlinkability of the templates along with improved privacy of the biometric data. Additionally, we also test the proposed hashing function for an image retrieval application using a benchmark dataset. The main goal of this paper is to develop a method for integrating multimodal fusion, deep hashing, and biometric security, with an emphasis on structural data from modalities like face and iris. The proposed approach is in no way a general biometric security framework that can be applied to all biometric modalities, as further research is needed to extend the proposed framework to other unconstrained biometric modalities.