The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
InfoResearchPeer-reviewed
Toward High Accuracy and Strong Security: Cancellable Templates for Multimodal Biometric Recognition Based on Feature Fusion
- Published
- Record updated
Summary
The authors propose a template protection framework for multimodal palmprint and palmvein biometrics. Their feature-level fusion network, PalmSynNet, pairs a Multi-Scale Local Feature Extraction module with a Global Feature Fusion module, and a cancellable random projection method called SoftMax-based random projection (SoRP) generates protected templates. Evaluations on the PolyU, TJ, and CUMT databases report superior fusion recognition performance and satisfy irreversibility, revocability, unlinkability, and resistance to various attacks.