长期人脸生物特征研究揭示日间波动远大于长期变化
Longitudinal Study of Facial Biometrics at the BEZ: Temporal Variance Analysis
- 在受控环境下对400+人进行2.5年跟踪，分析人脸识别得分变化
- 日间识别分数波动幅度远超整个周期内的整体变化
- 为生物特征稳定性研究提供数据基础，适合安全系统设计者参考
本研究基于在生物特征评估中心（BEZ）开展的长期生物特征评估，历时两年半，对超过400名涵盖不同族裔、性别和年龄群体的参与者使用多种生物特征工具定期测试。研究基于符合通用数据保护条例（GDPR）的本地BEZ数据库，该库包含超过238,000个生物特征数据集，涵盖人脸、指纹等多种模态。采用先进的人脸识别算法分析长期比对得分，结果显示，个体每日间的识别得分波动程度远高于整个测量周期内的整体变化。这些发现凸显了在受控环境中长期追踪同一人生物特征的重要性，为未来生物特征数据分析的进步奠定了基础。
原文摘要 · Abstract (English)
This study presents findings from long-term biometric evaluations conducted at the Biometric Evaluation Center (bez). Over the course of two and a half years, our ongoing research with over 400 participants representing diverse ethnicities, genders, and age groups were regularly assessed using a variety of biometric tools and techniques at the controlled testing facilities. Our findings are based on the General Data Protection Regulation-compliant local bez database with more than 238.000 biometric data sets categorized into multiple biometric modalities such as face and finger. We used state-of-the-art face recognition algorithms to analyze long-term comparison scores. Our results show that these scores fluctuate more significantly between individual days than over the entire measurement period. These findings highlight the importance of testing biometric characteristics of the same individuals over a longer period of time in a controlled measurement environment and lays the groundwork for future advancements in biometric data analysis.
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