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Enhancement of the Adaptive Shape Variants Average Values by Using Eight Movement Directions for Multi-Features Detection of Facial Sketch

Journal of ICT Research and Applications (JICTRA) · Vol. 6 No. 1 (2013) · pp. 1–20

DOI
10.5614/itbj.ict.2012.6.1.1
ISSN
2338-5499, 2337-5787
Published
2013-09-13
Publisher
DRI - ITB
Language
eng

Abstract

This paper aims to detect multi features of a facial sketch by using a novel approach. The detection of multi features of facial sketch has been conducted by several researchers, but they mainly considered frontal face sketches as object samples. In fact, the detection of multi features of facial sketch with certain angle is very important to assist police for describing the criminal's face, when criminal's face only appears on certain angle. Integration of the maximum line gradient value enhancement and the level set methods was implemented to detect facial features sketches with tilt angle to 15 degrees. However, these methods tend to move towards non features when there are a lot of graffiti around the shape. To overcome this weakness, the author proposes a novel approach to move the shape by adding a parameter to control the movement based on enhancement of the adaptive shape variants average values with 8 movement directions. The experimental results show that the proposed method can improve the detection accuracy up to 92.74%.

Collaboration network of the authors

16 authors · 30 co-authorship links · 12 across different organisations

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

  • Institut Teknologi Sepuluh Nopember
  • Universitas Trunodjoyo Madura
  • Institut Sains dan Teknologi Terpadu Surabaya
  • Tokyo University of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Mochamad HariadiInstitut Teknologi Sepuluh Nopember3
Moh. Aries SyufagiAffiliation unknown2
Dwi Ratna SulistyaningrumInstitut Teknologi Sepuluh Nopember1
Hans JuwianthoInstitut Sains dan Teknologi Terpadu Surabaya1
Esther Irawati SetiawanInstitut Teknologi Sepuluh Nopember1
Joan SantosoInstitut Teknologi Sepuluh Nopember1
Diana PurwitasariInstitut Teknologi Sepuluh Nopember1
Abid Famasya AbdillahInstitut Teknologi Sepuluh Nopember1
Safitri JuanitaInstitut Teknologi Sepuluh Nopember1
I Ketut Eddy PurnamaInstitut Sains dan Teknologi Terpadu Surabaya1
Eko SetijadiInstitut Teknologi Sepuluh Nopember1
Alamsyah AlamsyahInstitut Teknologi Sepuluh Nopember1

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">Enhancement of the Adaptive Shape Variants Average Values by Using Eight Movement Directions for Multi-Features Detection of Facial Sketch</dc:title>
	<dc:creator>Muntasa, Arif</dc:creator>
	<dc:creator>Shopan, Mochammad Kautsar</dc:creator>
	<dc:creator>Purnomo, Mauridhi Hery</dc:creator>
	<dc:creator>Kunio, Kondo</dc:creator>
	<dc:description xml:lang="en-US">This paper aims to detect multi features of a facial sketch by using a novel approach. The detection of multi features of facial sketch has been conducted by several researchers, but they mainly considered frontal face sketches as object samples. In fact, the detection of multi features of facial sketch with certain angle is very important to assist police for describing the criminal's face, when criminal's face only appears on certain angle. Integration of the maximum line gradient value enhancement and the level set methods was implemented to detect facial features sketches with tilt angle to 15 degrees. However, these methods tend to move towards non features when there are a lot of graffiti around the shape. To overcome this weakness, the author proposes a novel approach to move the shape by adding a parameter to control the movement based on enhancement of the adaptive shape variants average values with 8 movement directions. The experimental results show that the proposed method can improve the detection accuracy up to 92.74%.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2013-09-13</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/218</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.2012.6.1.1</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 6 No. 1 (2012); 1-20</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/218/212</dc:relation>
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