Rank-1 Identity Consensus Predicts Gallery Enrollment in 1:N Face Matching More Accurately than Score Thresholding

2026-07-14

AI Summary

This work addresses the vulnerability of fixed-score thresholds in 1:N face recognition, which are highly sensitive to variations in image quality or gallery composition and can lead to high-risk misidentifications. The authors propose a threshold-free rank-1 identity consistency mechanism (1-consistency), which determines whether a query subject is enrolled by requiring unanimous agreement among multiple independently trained matchers on the same top-ranked identity. This approach achieves, for the first time, a registration decision based solely on ranking consensus without any preset threshold. Comprehensive evaluation across 36 combinations of gallery and probe image qualities demonstrates that 1-consistency matches or even surpasses the performance of an Oracle-optimal threshold—without any parameter tuning—even under severely degraded probe conditions. When 1-consistency affirms enrollment, its correct matching rate reaches 97–100%, substantially outperforming the Oracle threshold’s 66–84%.

Abstract

In operational 1:N face identification, a crucial question arises for each probe: is this person enrolled in the gallery or not? The stakes are high and asymmetric. Rejecting a mate-present (MP) probe loses a valid lead; accepting a mate-absent (MA) probe makes every returned candidate a false identification, at worst a wrongful arrest. Most approaches threshold match scores, but scores shift substantially with image quality and gallery size and composition, making thresholds fixed before deployment brittle under realistic conditions. Our prior work introduced 1-consistency, the only method based on rank consensus across multiple independently trained matchers: a probe is labeled MP if all matchers return the same rank-1 identity. This work stress-tests 1-consistency across 36 (gallery, probe quality) scenarios spanning four quality levels and two structural axes: images per identity and total enrolled identities. We benchmark against two score-thresholding methods that bracket what any deployed threshold could achieve. Fixed Score-Thresholding (FST), calibrated once on baseline conditions, collapses asymmetrically as quality degrades: MP recall falls below 2% while MA recall holds near 100%. Oracle Score-Thresholding (OST), re-tuned per scenario, is the best any threshold could theoretically do, yet for degraded probes 1-consistency matches it with zero tuning. The two differ mainly in error type (OST favors MP recall, 1-consistency favors MA recall), but on one axis 1-consistency does not merely match the oracle: when it labels a probe MP, it returns the correct mate 97-100% of the time versus OST's 66-84% under severe degradation. In short, 1-consistency delivers oracle-level accuracy without the impossible requirement: it sets no threshold, so it needs no advance knowledge of the conditions a probe will arrive in, which is what makes it usable.

Problem

• 1:N face identification
• gallery enrollment
• mate-present/mate-absent decision
• match score thresholding
• face recognition reliability

Innovation

• 1-consistency
• rank-1 identity consensus
• threshold-free face identification
• mate-present/mate-absent detection
• multi-matcher consensus