Feature-based Identity Unlearning Evaluation for Biometric Applications

Privacy preservation has become an important concern in modern biometric systems, where models are often trained on datasets containing sensitive personal information. Privacy regulations grant individuals the legal right to withdraw their biometric data, creating a strong demand for machine unlearning techniques that can reliably remove the influence of such data from trained models.

In this work, we investigate the problem of class (identity) unlearning within the context of deep biometric recognition. We introduce a novel evaluation metric (Unlearning Similarity Distribution Trade-Off (USDTO)), which quantifies the trade-off between effectively removing identity-specific information and maintaining the integrity of the remaining feature distributions of unlearning at the feature-template level. We evaluate multiple unlearning strategies using the proposed metric and identify the optimal unlearning stage for each method.