Multibiometric Secure System Based on Deep Learning

Understand

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.