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

Hicham Johra; Rikke Gade; Mathias Østergaard Poulsen; Albert Daugbjerg Christensen; Mandana Sarey Khanie; Thomas Moeslund; Rasmus Lund Jensen

doi:10.1088/1742-6596/2042/1/012008

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

Hicham Johra^*, Rikke Gade, Mathias Østergaard Poulsen, Albert Daugbjerg Christensen, Mandana Sarey Khanie, Thomas Moeslund, Rasmus Lund Jensen

^*Corresponding author for this work

Aalborg University

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

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.

Original language	English
Article number	012008
Book series	Journal of Physics: Conference Series
Volume	2042
Issue number	1
Number of pages	7
ISSN	1742-6596
DOIs	https://doi.org/10.1088/1742-6596/2042/1/012008
Publication status	Published - 2021
Event	CISBAT 2021: Carbon Neutral Cities - Energy Efficiency & Renewables in the Digital Era - Lausanne, Switzerland Duration: 8 Sept 2021 → 10 Sept 2021

Conference

Conference	CISBAT 2021
Country/Territory	Switzerland
City	Lausanne
Period	08/09/2021 → 10/09/2021

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10.1088/1742-6596/2042/1/012008

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title = "Artificial Intelligence for Detecting Indoor Visual Discomfort from Facial Analysis of Building Occupants",

abstract = "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{\textquoteright}s face. The occupant{\textquoteright}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.",

author = "Hicham Johra and Rikke Gade and Poulsen, {Mathias {\O}stergaard} and Christensen, {Albert Daugbjerg} and Khanie, {Mandana Sarey} and Thomas Moeslund and Jensen, {Rasmus Lund}",

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T1 - Artificial Intelligence for Detecting Indoor Visual Discomfort from Facial Analysis of Building Occupants

AU - Johra, Hicham

AU - Gade, Rikke

AU - Poulsen, Mathias Østergaard

AU - Christensen, Albert Daugbjerg

AU - Khanie, Mandana Sarey

AU - Moeslund, Thomas

AU - Jensen, Rasmus Lund

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

U2 - 10.1088/1742-6596/2042/1/012008

DO - 10.1088/1742-6596/2042/1/012008

M3 - Journal article

SN - 1742-6596

VL - 2042

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 1

M1 - 012008

T2 - CISBAT 2021

Y2 - 8 September 2021 through 10 September 2021

ER -

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

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