面向国家身份证系统中年龄欺诈检测的面部年龄估计
Facial Age Estimation for Age Fraud Detection in National ID Systems
- UIDAI（印度唯一身份识别管理局）
- IIIT Hyderabad（海得拉巴国际信息技术学院）
- Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出SwinAge面部年龄估计系统，用于Aadhaar国家身份系统注册中检测虚报年龄欺诈，基于SwinFace架构在145万图像上训练，在1%FAR下各年龄阈值FRR为3%、0.4%、11%，MAE达2.94年，优于多个基准模型。
AI中文摘要：
在生物识别注册和更新过程中的身份欺诈仍然是大规模国家身份系统面临的主要挑战。一个常见的欺诈手段是虚报年龄，以获取受年龄限制的服务或福利计划。在这项工作中，我们提出了SwinAge，一个面部年龄估计系统，设计用于Aadhaar生物识别注册流程中，以协助质量检查（QC）操作员标记潜在的年龄相关欺诈。这对于像Aadhaar（世界上最大的国家身份识别项目）这样的系统至关重要，该系统持有约15亿个唯一身份，过去一年有2240万次新注册和2.83亿次更新。基于具有基于地标的相似性（扭曲仿射）对齐的SwinFace架构，我们在一个包含145万张面部图像的大型内部数据集上训练，并在一个独立的、按年龄分层的28.3万张图像测试集上评估，两者均来自一个包含71.6万个不同种族的独特受试者的群体。我们研究了三个Aadhaar特定的操作阈值（5岁、18岁和60岁），并提出了一个部署分流框架，将可疑病例标记为人工审查。遵循NIST FATE，我们报告每个阈值下的错误接受率/错误拒绝率（FAR/FRR），而不是总体准确率：在1%的FAR下，模型在<5岁、>18岁和>60岁年龄段的FRR分别为3%、0.4%和11.0%。SwinAge在同一测试集上实现了2.94年的平均绝对误差（MAE），在所有基准测试中优于三个零样本视觉语言模型，并在7个公共基准数据集中的5个上改进了最先进水平。我们进一步报告了按性别划分的误差，并为国家身份识别项目总结了经验教训。
英文摘要：
Identity fraud during biometric enrollment and updates remains a major challenge for large-scale national identity systems. A common fraud vector is misrepresenting one's age to access age-restricted services or welfare schemes. In this work, we present SwinAge, a facial age estimation system designed for use within the Aadhaar biometric enrollment pipeline, to assist quality-check (QC) operators to flag potential age-related fraud. This is critical for a system like Aadhaar (the world's largest national identity programme), that holds about 1.5 billion unique identities, with 22.4 million new enrollments and 283 million updates in the last year. Building upon the SwinFace architecture with landmark-based similarity (warp affine) alignment, we train on a large in-house dataset of 1.45 million face images and evaluate on an independent, age-stratified test set of 283K images, both drawn from an ethnically diverse population of 716K unique subjects. We investigate three Aadhaar-specific operational thresholds (5, 18, and 60 years) and propose a deployment triage framework that flags suspected cases for manual review. Following NIST FATE, we report false acceptance/rejection rates (FAR/FRR) at each threshold rather than aggregate accuracy: at 1% FAR the model achieves an FRR of 3% (<5yrs), 0.4% (>18yrs) and 11.0% (>60yrs). SwinAge achieves a mean absolute error (MAE) of 2.94 years on the same test set, outperforming three zero-shot vision language models on all benchmarks, and improving the state-of-the-art on 5 out of 7 public benchmark datasets. We further report per-gender errors and distill lessons for national identity programs.
