低分辨率人脸下人类识别准确率下降至随机水平，影响整体系统性能
Peepers & Pixels: Human Recognition Accuracy on Low Resolution Faces
- 测试不同像素间距（IPD）下人工识别准确率
- IPD为10像素时准确率仅50.7%，5像素时降至35.9%
- 即使自信度高，人类仍难辨认低分辨率人脸
自动化一对一（1:N）人脸识别是执法部门常用的重要调查工具。在该流程中，自动化识别结果需经人工审查后方可作为线索使用。尽管理想条件下自动化识别可接近完美，但实际应用常依赖监控图像，其质量常受多种因素影响。其中关键因素为图像分辨率，通常以人脸瞳距（IPD）衡量，即两眼间像素数。低IPD会显著降低自动化识别精度。然而，人类在低IPD下的识别可靠性阈值尚未明确。本研究系统评估了不同IPD下的人类识别准确率。结果显示，在10像素和5像素时，人类准确率分别仅为50.7%和35.9%，已接近或低于随机水平，而决策自信度仍分别达77%和70.7%。表明低分辨率图像下，人工识别能力可能成为整个系统性能的瓶颈。
原文摘要 · Abstract (English)
Automated one-to-many (1:N) face recognition is a powerful investigative tool commonly used by law enforcement agencies. In this context, potential matches resulting from automated 1:N recognition are reviewed by human examiners prior to possible use as investigative leads. While automated 1:N recognition can achieve near-perfect accuracy under ideal imaging conditions, operational scenarios may necessitate the use of surveillance imagery, which is often degraded in various quality dimensions. One important quality dimension is image resolution, typically quantified by the number of pixels on the face. The common metric for this is inter-pupillary distance (IPD), which measures the number of pixels between the pupils. Low IPD is known to degrade the accuracy of automated face recognition. However, the threshold IPD for reliability in human face recognition remains undefined. This study aims to explore the boundaries of human recognition accuracy by systematically testing accuracy across a range of IPD values. We find that at low IPDs (10px, 5px), human accuracy is at or below chance levels (50.7%, 35.9%), even as confidence in decision-making remains relatively high (77%, 70.7%). Our findings indicate that, for low IPD images, human recognition ability could be a limiting factor to overall system accuracy.
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