Abstract
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.
Abstract (translated)
自动化的一对多（1:N）人脸识别是一项强大的调查工具，通常由执法机构使用。在这种情况下，自动1:N识别产生的潜在匹配结果会在用作调查线索之前由人工检查员进行审查。虽然在理想的成像条件下，自动化1:N识别可以实现接近完美的准确度，但在实际操作场景中可能需要使用监控图像，这些图像往往在多个质量维度上都存在退化问题。其中一个重要的质量维度是图像分辨率，通常通过面部像素的数量来量化。衡量这一指标的常用方法是两眼之间的距离（IPD），即瞳孔间的像素数。低IPD已知会降低自动化人脸识别的准确度，然而，人类在低IPD条件下的识别可靠性的阈值仍未明确定义。 本研究旨在通过对一系列不同IPD值的人脸图像进行系统测试来探索人类识别准确率的边界。我们发现，在低IPD（10px、5px）的情况下，人类识别准确率甚至低于随机水平（50.7%，35.9%），尽管在此情况下决策的信心依然相对较高（77%，70.7%）。我们的研究结果表明，对于低IPD图像而言，人类的识别能力可能成为整体系统准确度的一个限制因素。
URL
https://arxiv.org/abs/2503.20108
