Facial expression recognition, a predictive tool for perceiving urban open space environments under audio-visual interaction

Abstract

The quality of soundscape in urban open spaces is important to wellbeing. An AI-based facial expression recognition method has the potential to be a useful tool for predicting urban sound perception since it can correctly and in real time gather human perception data from facial photos. The purpose of this study is to provide classification criteria, time dimension data processing and indicator screening methods for facial expression recognition-based soundscape prediction. The study captured typical urban audiovisual environments and recorded facial expression data from subjects in a laboratory setting. The results of the study showed that there were significant differences in the effects of the three types of visual environments and the three types of acoustic environments on the subjects’ facial expressions, and that the effect of acoustic environments on visual perception (η2 0.067 on average) was generally greater than the effect of visual environments on acoustic perception (η2 0.035 on average). A linear regression model for predicting acoustic perception was developed based on audiovisual interactions, dynamic change patterns, and indicator selection. The results of this paper help to realize another perceptual dimension in the establishment of smart city and help achieve sustainability.