Systematic Face Pareidolia Generation Method Using Cycle‐Consistent Adversarial Networks

Abstract

Pareidolia is a psychological tendency of perceiving a face in non‐face stimulus. As a majority of people globally experience this tendency, it has been extensively studied and measured in terms of tendencies, such as frequencies. However, no study has investigated the systematic manipulation of stimulus owing to the lack of a systematic image‐generation method. Therefore, herein, we generated face pareidolia stimuli using a face data set with annotated data. We employed cycle‐consistent adversarial networks (CycleGAN), an image‐to‐image‐style translation framework, to generate stimuli for translating natural‐image styles from face images. We manipulated the weight of the cycle‐consistency loss in the CycleGAN via an experiment to evaluate the image generated using the CycleGAN. Thus, we found that the weight value of the evaluation experiment correlated with the pareidolia‐inducing power when the preprocessing of the face data set was applied to the blur process. As a result, we achieved to systematically generate pareidolia stimuli. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.