量化人脸二值模板随年龄变化的比特漂移，发现码长越短越稳定。
Ageing Drift in Binary Face Templates: A Bits-per-Decade Analysis
- 用PCA-ITQ将浮点特征压缩为64/128位二值码，分析跨年龄距离变化。
- 64位模板每十年漂移1.357比特，128位模板每十年漂移2.571比特。
- 适合部署在智能卡等对稳定性要求高的场景，可定期重注册缓解漂移。
我们研究了紧凑二值人脸模板的纵向稳定性，并直接以每十年比特数量化年龄漂移。使用现代人脸CNN生成的浮点嵌入，经PCA-ITQ压缩为64位和128位代码。针对AgeDB中至少有三个不同年龄的566个身份，构建所有真实配对，并为每个身份拟合汉明距离与绝对年龄差的线性模型。64位模板的中位斜率为每十年1.357比特，128位模板为每十年2.571比特，95%非参数置信区间紧密。分布主要为正，表明类内距离随时间有微小但系统性增加。由于漂移随码长增加，固定决策阈值下短码更抗老化。通过报告三个年龄区间下FAR=1%时的EER和TPR，将斜率与运行特性关联。讨论了对智能卡及片上匹配部署的影响，提出周期性重新注册和针对性修正不稳比特位等简单缓解策略。代码与CSV数据集已提供以支持复现。
原文摘要 · Abstract (English)
We study the longitudinal stability of compact binary face templates and quantify ageing drift directly in bits per decade. Float embeddings from a modern face CNN are compressed with PCA-ITQ into 64- and 128-bit codes. For each identity in AgeDB with at least three distinct ages, we form all genuine pairs and fit a per-identity linear model of Hamming distance versus absolute age gap. Across 566 identities, the median slope is 1.357 bits per decade for 64-bit templates and 2.571 bits per decade for 128-bit templates, with tight non-parametric 95 percent bootstrap confidence intervals. The distributions are predominantly positive, indicating a small but systematic increase in intra-class distance over time. Because drift scales with code length, shorter codes are inherently more age-stable at a fixed decision threshold. We connect these slopes to operating characteristics by reporting EER and TPR at FAR = 1 percent in three age bins. We discuss implications for smart-card and match-on-card deployments, including simple mitigations such as periodic re-enrolment and targeted parity on empirically unstable bit positions. Code and CSV artifacts are provided to support reproducibility.
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