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Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 4 (2020) · pp. 407–416

DOI
10.22146/ijccs.59672
ISSN
2460-7258, 1978-1520
Published
2020-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.

Keywords

Collaboration network of the authors

26 authors · 52 co-authorship links

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Barlian Henryranu PrasetioUniversity of Brawijaya3
Rizal MaulanaUniversity of Brawijaya3
Eko SetiawanUniversity of Brawijaya2
Eko SetiawanUniversity of Brawijaya2
Agung Setia BudiUniversity of Brawijaya2
Dahnial SyauqyUniversity of Brawijaya2
Gembong Edhi SetyawanUniversity of Brawijaya1
Benny AdiwijayaUniversity of Brawijaya1
Faizal Andy SusiloUniversity of Brawijaya1
Ryzaldi Ananda FabianaUniversity of Brawijaya1
Syahrul ChilmiUniversity of Brawijaya1
Septiyo Budi PerkasaUniversity of Brawijaya1
Dittha RatanasariUniversity of Brawijaya1
Fahmi ErzaUniversity of Brawijaya1
Ragil Hadi PrasetyoUniversity of Brawijaya1
Achmad GhifariUniversity of Brawijaya1
Gabriel Hizkia Marhata SimbolonAffiliation unknown1
Gabriel Hizkia Marhata SimbolonAffiliation unknown1
Teguh Surya Darma SantosoAffiliation unknown1
Syifa’ Hukma ShabiyyaAffiliation unknown1
Muhammad Rava AthallaAffiliation unknown1
Raihan Adi PrakosoAffiliation unknown1
Ika NurfaridaAffiliation unknown1
Alifa Azwadina FuadAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry</dc:title>
	<dc:creator>Fitriyah, Hurriyatul</dc:creator>
	<dc:creator>Widasari, Edita Rosana</dc:creator>
	<dc:subject xml:lang="en-US">Computer Vision</dc:subject>
	<dc:subject xml:lang="en-US">Face detection; Thermal images; Frontal view; Facial geometry</dc:subject>
	<dc:description xml:lang="en-US"> Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2020-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/59672</dc:identifier>
	<dc:identifier>10.22146/ijccs.59672</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 4 (2020): October; 407-416</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/59672/29703</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>