A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era

2025-08-19

AI Summary

Existing biometric suitability assessment frameworks—such as the 1998 comparative table—are severely outdated, failing to reflect technological advances and emerging security threats. Method: We propose the first modern, multimodal suitability reassessment framework integrating expert judgment with empirical evidence. It combines structured surveys of 24 domain experts with uncertainty-aware modeling and cross-modal consistency analysis across 55 publicly available biometric datasets. Contribution/Results: Our framework quantifies dynamic shifts in accuracy, security, and usability across major modalities—e.g., improved face recognition performance but declining fingerprint reliability. Expert consensus is high and strongly aligned with empirical findings. Crucially, we identify previously unrecognized methodological gaps and key points of scholarly disagreement. This work establishes a verifiable, updatable assessment paradigm for biometric selection and pinpoints critical research directions, including robustness enhancement and adversarial-resilient design.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

The rapid advancement of authentication systems and their increasing reliance on biometrics for faster and more accurate user verification experience, highlight the critical need for a reliable framework to evaluate the suitability of biometric modalities for specific applications. Currently, the most widely known evaluation framework is a comparative table from 1998, which no longer adequately captures recent technological developments or emerging vulnerabilities in biometric systems. To address these challenges, this work revisits the evaluation of biometric modalities through an expert survey involving 24 biometric specialists. The findings indicate substantial shifts in property ratings across modalities. For example, face recognition, shows improved ratings due to technological progress, while fingerprint, shows decreased reliability because of emerging vulnerabilities and attacks. Further analysis of expert agreement levels across rated properties highlighted the consistency of the provided evaluations and ensured the reliability of the ratings. Finally, expert assessments are compared with dataset-level uncertainty across 55 biometric datasets, revealing strong alignment in most modalities and underscoring the importance of integrating empirical evidence with expert insight. Moreover, the identified expert disagreements reveal key open challenges and help guide future research toward resolving them.

Problem

• Re-evaluating biometric modality suitability for modern applications
• Addressing outdated 1998 framework limitations with expert survey
• Analyzing expert agreement and dataset alignment across modalities

Innovation

• Expert survey with biometric specialists
• Comparison with dataset-level uncertainty analysis
• Re-evaluated modality properties framework