在事物中看到面孔：一种用于类人症的模型和数据集

Seeing Faces in Things: A Model and Dataset for Pareidolia

September 24, 2024

作者: Mark Hamilton, Simon Stent, Vasha DuTell, Anne Harrington, Jennifer Corbett, Ruth Rosenholtz, William T. Freeman

cs.AI

摘要

人类视觉系统经过良好调整，能够检测各种形状和大小的面孔。虽然这带来明显的生存优势，比如更容易在丛林中发现未知的捕食者，但也会导致虚假的面部检测。"面孔错觉"描述了在其他随机刺激中察觉到类似面孔结构的现象：比如在咖啡渍或天空中看到面孔。本文从计算机视觉的角度研究了面孔错觉。我们提出了一个“物中面”图像数据集，包括五千张网络图像，其中包含人工标注的错觉面孔。利用这个数据集，我们检验了最先进的人脸检测器展现的错觉现象程度，并发现人类和机器之间存在显著的行为差距。我们发现，人类需要检测动物面孔以及人类面孔的进化需求可能解释了部分差距。最后，我们提出了一个关于图像中错觉的简单统计模型。通过对人类受试者和我们的错觉面孔检测器的研究，我们确认了我们模型关于哪些图像条件最有可能诱发错觉的一个关键预测。数据集和网站：https://aka.ms/faces-in-things

English

The human visual system is well-tuned to detect faces of all shapes and sizes. While this brings obvious survival advantages, such as a better chance of spotting unknown predators in the bush, it also leads to spurious face detections. ``Face pareidolia'' describes the perception of face-like structure among otherwise random stimuli: seeing faces in coffee stains or clouds in the sky. In this paper, we study face pareidolia from a computer vision perspective. We present an image dataset of ``Faces in Things'', consisting of five thousand web images with human-annotated pareidolic faces. Using this dataset, we examine the extent to which a state-of-the-art human face detector exhibits pareidolia, and find a significant behavioral gap between humans and machines. We find that the evolutionary need for humans to detect animal faces, as well as human faces, may explain some of this gap. Finally, we propose a simple statistical model of pareidolia in images. Through studies on human subjects and our pareidolic face detectors we confirm a key prediction of our model regarding what image conditions are most likely to induce pareidolia. Dataset and Website: https://aka.ms/faces-in-things