用排名一致性判断人脸是否在库中，比传统阈值更准且无需调参。
Rank-1 Identity Consensus Predicts Gallery Enrollment in 1:N Face Matching More Accurately than Score Thresholding
- 通过多个独立模型的排名一致判定探针是否在库中。
- 在图像质量差时仍能保持97%-100%正确匹配率，优于传统阈值方法。
- 无需预先知道部署环境，适合真实场景下高可靠性人脸识别。
在实际1:N人脸识别中，关键问题是：该待识别者是否已在库中？错误代价极高且不对称：遗漏在库者会丢失线索，误认则导致全部结果为假阳性，最坏情况可能引发误捕。现有方法多依赖匹配得分阈值，但得分受图像质量、库大小与结构影响显著，固定阈值在真实条件下极易失效。本文基于前期提出的1-一致性（1-consistency）方法——即多个独立训练的匹配器均返回同一身份为第一结果时，才判定该探针在库中——在36种（库结构，探针质量）组合下进行压力测试，覆盖四类图像质量及两个结构性维度：每身份图像数与总人数。对比两种阈值方法：仅在基线校准一次的固定阈值（FST），随质量下降性能崩溃，对在库探针召回率降至2%以下；理想阈值（OST）每场景重新调优，代表阈值理论上限。结果显示，即使在严重退化情况下，1-一致性仍可达到与OST相当的准确率，且无需任何调参。两者主要差异在于错误类型偏好（OST更保在库召回，1-一致性更保不在库召回），但在某轴上，1-一致性表现超越：当判定在库时，其正确匹配率达97%-100%，而OST仅为66%-84%。综上，1-一致性在无预知条件情况下实现接近最优的精度，具有实用价值。
原文摘要 · Abstract (English)
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.
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