Seeing Faces in Things: A Model and Dataset for Pareidolia
Authors: name: Mark Hamilton, name: Simon Stent, name: Vasha DuTell, name: Anne Harrington, name: Jennifer Corbett, name: Ruth Rosenholtz, name: William T. Freeman

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