在事物中看見臉孔：一個用於錯覺的模型和數據集

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

人類的視覺系統對各種形狀和大小的臉部都有很好的識別能力。儘管這帶來明顯的生存優勢，例如更容易在叢林中發現未知的捕食者，但也會導致誤檢臉部。"面孔錯覺"描述了在其他隨機刺激中看到類似面孔結構的知覺現象：比如在咖啡漬或天空中的雲朵中看到臉部。在本文中，我們從計算機視覺的角度研究面孔錯覺。我們提出了一個包含五千張網絡圖像並由人類標註的錯覺性面孔的圖像數據集“物中之面”。利用這個數據集，我們檢驗了最先進的人臉檢測器展現出的錯覺現象程度，並發現人類和機器之間存在顯著的行為差距。我們發現人類需要識別動物臉部以及人類臉部的進化需求可能解釋了這種差距的一部分。最後，我們提出了一個關於圖像中錯覺現象的簡單統計模型。通過對人類受試者和我們的錯覺性臉部檢測器的研究，我們確認了我們的模型對於哪些圖像條件最有可能誘發錯覺的一個關鍵預測。數據集和網站：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