Simulating Pareidolia of Faces for Architectural Image Analysis

Abstract

The hypothesis of the present study is that features of abstract face-like patterns can be perceived in the architectural design of selected house fac¸ades and trigger emotional responses of observers. In order to simulate this phenomenon, which is a form of pareidolia, a software system for pattern recognition based on statistical learning was applied. One-class classification was used for face detection and an eight-class classifier was employed for facial expression analysis. The system was trained by means of a database consisting of 280 frontal images of human faces that were normalised to the inner eye corners. A separate set of test images contained human facial expressions and selected house fac¸ades. The experiments demonstrated how facial expression patterns associated with emotional states such as surprise, fear, happiness, sadness, anger, disgust, contempt or neutrality could be identified in both types of test images, and how the results depended on preprocessing and parameter selection for the classifiers.