📄 中文摘要
高端人脸识别模型在从受控的NIST数据集转移到原始CCTV录像时，准确率可能会下降30%，而无需更改任何代码。虽然“99%准确”的声明在GitHub README或市场宣传册上看起来很吸引人，但这通常是对高分辨率、正面护照照片的实验室性能测量。在实际应用中，传感器噪声、激进的H.264压缩和偏离轴心的角度等变量造成了技术差距，这一点大多数开发者和调查人员未能考虑到。
“99%准确”的人脸识别技术对您的案件意味着什么
出处: What "99% Accurate" Facial Recognition Actually Means for Your Case
发布: 2026年3月15日
📄 English Summary
What "99% Accurate" Facial Recognition Actually Means for Your Case
Moving a top-tier facial recognition model from a controlled NIST dataset to raw CCTV footage can trigger a 30% drop in accuracy without changing a single line of code. While a '99% accuracy' claim looks impressive on a GitHub README or marketing brochure, it often represents a laboratory measurement of performance on high-resolution, front-facing passport photos. In real-world scenarios, variables such as sensor noise, aggressive H.264 compression, and off-axis angles create a technical gap that most developers and investigators fail to account for.