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 B. 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 B. Moeslund, Rasmus Lund Jensen

Publikation: Bidrag til tidsskrift › Konferenceartikel i tidsskrift › Forskning › peer review

3 Citationer (Scopus)

59 Downloads (Pure)

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.

Originalsprog	Engelsk
Artikelnummer	012008
Bogserie	Journal of Physics: Conference Series (Online)
Vol/bind	2042
Udgave nummer	1
ISSN	1742-6596
DOI	https://doi.org/10.1088/1742-6596/2042/1/012008
Status	Udgivet - 2021
Begivenhed	CISBAT 2021 - Lausanne, Virtual, Schweiz Varighed: 8 sep. 2021 → 10 sep. 2021 https://cisbat.epfl.ch/call.html

Konference

Konference	CISBAT 2021
Land/Område	Schweiz
By	Lausanne, Virtual
Periode	08/09/2021 → 10/09/2021
Internetadresse	https://cisbat.epfl.ch/call.html

Emneord

AI
Visual Comfort
Glare
Machine Learning

Adgang til dokumentet

10.1088/1742-6596/2042/1/012008Licens: CC BY 3.0

Artificial Intelligence for Detecting Indoor Visual Discomfort from Facial Analysis of Building OccupantsForlagets udgivne version, 1,07 MBLicens: CC BY 3.0

https://iopscience.iop.org/article/10.1088/1742-6596/2042/1/012008/pdfLicens: CC BY 3.0

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CISBAT 2021 - Presentation Hicham Johra

1 Afsluttet

AI for optimizing indoor visual comfort from facial analysis
Johra, H., Moeslund, T. B., Gade, R., Jensen, R. L., Petrova, E. A. & Rohde, L.
01/01/2021 → 30/06/2021
Projekter: Projekt › Forskning

1 Organisering af eller deltagelse i workshop, kursus, seminar, udstilling eller lignende

AAU AI for the People Center - Talk
Hicham Johra (Deltager)
11 nov. 2021
Aktivitet: Deltagelse i faglig begivenhed › Organisering af eller deltagelse i workshop, kursus, seminar, udstilling eller lignende

Best Paper Award at Conference CISBAT 2021
Johra, Hicham (Modtager), Gade, Rikke (Modtager), Poulsen, Mathias Østergaard (Modtager), Christensen, Albert Daugbjerg (Modtager), Sarey Khanie, Mandana (Modtager), Moeslund, Thomas B. (Modtager) & Jensen, Rasmus Lund (Modtager), 9 sep. 2021
Pris: Konferencepriser

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BEST PAPER AWARD TO THE BRIDGING PROJECT
Hicham Johra
13/09/2021
1 Mediebidrag
Presse/medie

3 Citationer
1 Rapport

Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations
Johra, H., Veit, M., Poulsen, M. Ø., Christensen, A. D., Gade, R., Moeslund, T. B. & Jensen, R. L., jul. 2023, Aalborg: Department of the Built Environment, Aalborg University. 20 s. (DCE Technical Reports; Nr. 316).
Publikation: Bog/antologi/afhandling/rapport › Rapport › Forskning

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Citationsformater

@inproceedings{6124247ace324587808bd5490e38c356,

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.",

keywords = "AI, Visual Comfort, Glare, Machine Learning, AI, Visual Comfort, Glare, Machine Learning",

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

year = "2021",

doi = "10.1088/1742-6596/2042/1/012008",

language = "English",

volume = "2042",

journal = "Journal of Physics: Conference Series (Online)",

issn = "1742-6596",

publisher = "IOP Publishing",

number = "1",

note = "CISBAT 2021 ; Conference date: 08-09-2021 Through 10-09-2021",

url = "https://cisbat.epfl.ch/call.html",

}

TY - GEN

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 - Sarey Khanie, Mandana

AU - Moeslund, Thomas B.

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.

KW - AI

KW - Visual Comfort

KW - Glare

KW - Machine Learning

KW - AI

KW - Visual Comfort

KW - Glare

KW - Machine Learning

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

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

M3 - Conference article in Journal

SN - 1742-6596

VL - 2042

JO - Journal of Physics: Conference Series (Online)

JF - Journal of Physics: Conference Series (Online)

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

Abstract

Konference

Emneord

Adgang til dokumentet

AUB Link

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Fingeraftryk

Projekter

AI for optimizing indoor visual comfort from facial analysis

Aktiviteter

AAU AI for the People Center - Talk

Priser

Best Paper Award at Conference CISBAT 2021

Presse/Medier

BEST PAPER AWARD TO THE BRIDGING PROJECT

Publikation

Training and testing labelled image and video datasets of human faces for different indoor visual comfort and glare visual discomfort situations

Citationsformater