通过局部密度感知提升生物特征识别在开放集场景下的准确性。
LocalScore: Local Density-Aware Similarity Scoring for Biometrics
- 基于k近邻计算画廊特征的局部密度，动态调整相似性评分。
- 开放集检索误报率降低13个百分点，验证真阳性率提升23个百分点。
- 无需修改模型架构，可直接嵌入现有系统，适合实际部署。
开放集生物特征识别面临探测样本未在画廊中注册的挑战，传统系统难以有效识别非匹配样本。尽管多样本画廊在实际应用中日益普遍，但现有方法通常将个体内部差异压缩为单一全局表征，导致决策边界不佳、开放集鲁棒性差。为此，我们提出LocalScore——一种简单有效的打分算法，通过k-近邻显式建模画廊特征分布的局部密度。该方法与网络架构和损失函数无关，计算开销极低，可作为即插即用模块集成到现有生物特征系统中。大量跨模态实验表明，LocalScore在开放集检索（FNIR@FPIR从53%降至40%）和验证（TAR@FAR从51%提升至74%）任务中均实现显著性能提升。我们进一步提供了理论分析与实证验证，揭示了该方法在特定数据集特性下效果最优的原因。
原文摘要 · Abstract (English)
Open-set biometrics faces challenges with probe subjects who may not be enrolled in the gallery, as traditional biometric systems struggle to detect these non-mated probes. Despite the growing prevalence of multi-sample galleries in real-world deployments, most existing methods collapse intra-subject variability into a single global representation, leading to suboptimal decision boundaries and poor open-set robustness. To address this issue, we propose LocalScore, a simple yet effective scoring algorithm that explicitly incorporates the local density of the gallery feature distribution using the k-th nearest neighbors. LocalScore is architecture-agnostic, loss-independent, and incurs negligible computational overhead, making it a plug-and-play solution for existing biometric systems. Extensive experiments across multiple modalities demonstrate that LocalScore consistently achieves substantial gains in open-set retrieval (FNIR@FPIR reduced from 53% to 40%) and verification (TAR@FAR improved from 51% to 74%). We further provide theoretical analysis and empirical validation explaining when and why the method achieves the most significant gains based on dataset characteristics.
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