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The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

Journal of ICT Research and Applications (JICTRA) · Vol. 15 No. 3 (2021) · pp. 251–264

DOI
10.5614/itbj.ict.res.appl.2021.15.3.4
ISSN
2338-5499, 2337-5787
Published
2021-12-28
Publisher
DRI - ITB
Language
eng

Abstract

Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.

Keywords

Collaboration network of the authors

11 authors · 23 co-authorship links · 9 across different organisations

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

  • Satya Wacana Christian University
  • Universitas Raharja
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ivanna K. TimotiusSatya Wacana Christian University2
Untung RahardjaUniversitas Raharja1
Ninda LutfianiUniversitas Raharja1
Irwan SembiringSatya Wacana Christian University1
Adi SetiawanSatya Wacana Christian University1
Sulistio SulistioUniversitas Raharja1
Andreas Ardian FebriantoSatya Wacana Christian University1
Regina LionnieSatya Wacana Christian University1

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://journals.itb.ac.id/index.php/jictra/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">The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition</dc:title>
	<dc:creator>Abednego, Septian</dc:creator>
	<dc:creator>Setyawan, Iwan</dc:creator>
	<dc:creator>Dewantoro, Gunawan</dc:creator>
	<dc:subject xml:lang="en-US">gender recognition</dc:subject>
	<dc:subject xml:lang="en-US">kNN</dc:subject>
	<dc:subject xml:lang="en-US">MLLPQ</dc:subject>
	<dc:subject xml:lang="en-US">QLRBP</dc:subject>
	<dc:subject xml:lang="en-US">SVM</dc:subject>
	<dc:description xml:lang="en-US">Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2021-12-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
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	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/15650</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2021.15.3.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 15 No. 3 (2021); 251-264</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:source>10.5614/itbj.ict.res.appl.2021.15.3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/15650/5561</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 Journal of ICT Research and Applications</dc:rights>
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