DeepShieldFed: Securing Face Templates via Deep Cancelable Transforms and Federated Learning

The adoption of facial recognition technology for identity verification has seen significant growth in recent years. While state-of-the-art (SOTA) facial recognition systems demonstrate high accuracy, the biometric features extracted and stored in system databases are inherently privacy-sensitive. If compromised, this data poses a serious threat to user privacy. To address this challenge, we propose a novel federated learning-based cancelable template protection framework that combines secure, user-specific transformations with collaborative training. In our approach, each user’s face features are first converted into a cancelable template, revocable representation, and then used to train a shared model through federated learning, which enables multiple clients to jointly update a global model without sharing their raw or transformed biometric data.