Toward High Accuracy and Strong Security: Cancellable Templates for Multimodal Biometric Recognition Based on Feature Fusion

This research proposes a secure system for multimodal biometric recognition (using multiple biological measurements like palmprints and vein patterns together) that protects user privacy by creating cancellable templates (protected versions of biometric data that can't be reversed to recover the original). The system uses a neural network to combine features from different biometric sources and a novel random projection method to generate templates that are irreversible, revocable (can be replaced if compromised), and resistant to various attacks.