Biometric Confusion Matrix and Inter ZooPlot: Two Novel Visualizations for Biometric Verification Evaluation

Biometric Verification Systems (BVS) often suffer from misclassification errors, which are frequently concentrated around a small subset of users for whom the system performs poorly. The Biometric Menagerie was introduced to categorize such users based on their biometric behavior. However, its main representation, the ZooPlot, fails to accurately distinguish all categories defined in the original Menagerie, particularly "Lambs" (easily impersonated) and "Wolves" (frequently impersonate others).This paper proposes two new visualizations for evaluating BVS: Inter ZooPlot and the Biometric Confusion Matrix (BCM). A user study was conducted to assess the effectiveness of different visualizations. Inter ZooPlot and BCM achieved average accuracies of 90.0% and 89.4%, respectively, in distinguishing the four original categories defined in the Biometric Menagerie, outperforming the baseline ZooPlot at 73.9%. Furthermore, we show that BCM can reveal sources of user-specific errors and highlight system imbalances, making it a promising post-hoc explainability method for biometric system analysis. All additional materials are available at: https://github.com/Boyu1998/BCM.