Artificial Intelligence for Detecting Indoor Visual Discomfort from Facial Analysis of Building Occupants

Glare is a common local visual discomfort that is difficult to identify with conventional light sensors. This article presents an artificial intelligence algorithm that detects subjective local glare discomfort from the image analysis of the video footage of an office occupant’s face. The occupant’s face is directly used as a visual comfort sensor. Results show that it can recognize glare discomfort with around 90% accuracy. This algorithm can thus be at the basis of an efficient feedback control system to regulate shading devices in an office building.