title: Seeing Faces in Things: A Model and Dataset for Pareidolia
summary: 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
paper_authors._id: 66f3b3815294d1cc7c3256a3
paper_authors.name: Mark Hamilton
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-25T07:35:02.998Z
paper_authors._id: 62dae3734398e21bf7f53443
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1658512237806-noauth.jpeg
paper_authors.fullname: Mark Hamilton
paper_authors.type: user
paper_authors.user: mhamilton723
paper_authors._id: 66f3b3815294d1cc7c3256a4
paper_authors.name: Simon Stent
paper_authors._id: 66f3b3815294d1cc7c3256a5
paper_authors.name: Vasha DuTell
paper_authors._id: 66f3b3815294d1cc7c3256a6
paper_authors.name: Anne Harrington
paper_authors._id: 66f3b3815294d1cc7c3256a7
paper_authors.name: Jennifer Corbett
paper_authors._id: 66f3b3815294d1cc7c3256a8
paper_authors.name: Ruth Rosenholtz
paper_authors._id: 66f3b3815294d1cc7c3256a9
paper_authors.name: William T. Freeman
paper_title: Seeing Faces in Things: A Model and Dataset for Pareidolia
paper_summary: 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
paper_ai_keywords: human face detector state-of-the-art human face detector pareidolic faces pareidolic face detectors