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.
A secure system for multi-biometric data has been proposed in REF that uses deep neural networks and error-correction coding.
Multibiometric secure system based on deep learning
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Computer Science Mathematics