Peepers & Pixels：低分辨率人脸的人类识别准确率
Peepers & Pixels: Human Recognition Accuracy on Low Resolution Faces
AI总结：
本研究系统测试不同瞳孔间距下人类对人脸识别的准确率，发现低分辨率（IPD为10px、5px）时人类准确率低于或接近随机水平，表明人类识别能力可能限制整体系统性能。
摘要：
自动一对多（1:N）人脸识别是一种强大的调查工具，常被执法机构使用。在此背景下，自动1:N识别产生的潜在匹配结果会在可能用作调查线索之前由人类审查员进行复核。虽然自动1:N识别在理想成像条件下可以达到接近完美的准确率，但操作场景可能需要使用监控图像，这些图像往往在各种质量维度上有所退化。其中一个重要的质量维度是图像分辨率，通常以人脸像素数量来衡量。衡量这一指标的常用度量是瞳孔间距（IPD），即瞳孔之间的像素数。已知低IPD会降低自动人脸识别的准确率。然而，人类人脸识别可靠性的IPD阈值尚未明确。本研究旨在通过系统测试一系列IPD值下的人类识别准确率，探索人类识别准确率的边界。我们发现，在低IPD（10px、5px）下，人类准确率处于或低于随机水平（50.7%、35.9%），即使决策信心相对较高（77%、70.7%）。我们的研究结果表明，对于低IPD图像，人类识别能力可能成为整体系统准确率的限制因素。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
