Melola Discovery

← Melola Discovery

Jurnal SINTA 2

Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System

Journal of ICT Research and Applications (JICTRA) · Vol. 10 No. 3 (2016) · pp. 209–227

DOI
10.5614/itbj.ict.res.appl.2016.10.3.2
ISSN
2338-5499, 2337-5787
Published
2016-10-31
Publisher
DRI - ITB
Language
eng

Abstract

Perfectly segmenting the area of the iris is one of the most important steps in iris recognition. There are several problematic areas that affect the accuracy of the iris segmentation step, such as eyelids, eyelashes, glasses, pupil (due to less accurate iris segmentation), motion blur, and lighting and specular reflections. To solve these problems, gradient magnitude and Fourier descriptor are employed to do iris segmentation in the proposed Multimodal Biometric Authentication System (MBAS). This approach showed quite promising results, i.e. an accuracy rate of 97%. The result of the iris recognition system was combined with the result of an open-source fingerprint recognition system to develop a multimodal biometrics authentication system. The results of the fusion between iris and fingerprint authentication were 99% accurate. Data from Multimedia Malaysia University (MMUI) and our own prepared database, the SGU-MB-1 dataset, were used to test the accuracy of the proposed system.

Collaboration network of the authors

8 authors · 18 co-authorship links · 11 across different organisations

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

  • Swiss German University
  • Institut Teknologi Sepuluh Nopember
  • Badan Pengkajian dan Penerapan Teknologi
  • Sebelas Maret University
  • Eijkman Institute for Molecular Biology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus Zainal ArifinInstitut Teknologi Sepuluh Nopember1
Riyanarto SarnoInstitut Teknologi Sepuluh Nopember1
Umi SalamahSebelas Maret University1
Ismail Eko Prayitno RoziAffiliation unknown1
Puji Budi Setia AsihEijkman Institute for Molecular Biology1

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) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Iris Segmentation using Gradient Magnitude and Fourier Descriptor for Multimodal Biometric Authentication System</dc:title>
	<dc:creator>Sulaeman, Defiana</dc:creator>
	<dc:creator>Nugroho, Anto Satriyo</dc:creator>
	<dc:creator>Galinium, Maulahikmah</dc:creator>
	<dc:description xml:lang="en-US">Perfectly segmenting the area of the iris is one of the most important steps in iris recognition. There are several problematic areas that affect the accuracy of the iris segmentation step, such as eyelids, eyelashes, glasses, pupil (due to less accurate iris segmentation), motion blur, and lighting and specular reflections. To solve these problems, gradient magnitude and Fourier descriptor are employed to do iris segmentation in the proposed Multimodal Biometric Authentication System (MBAS). This approach showed quite promising results, i.e. an accuracy rate of 97%. The result of the iris recognition system was combined with the result of an open-source fingerprint recognition system to develop a multimodal biometrics authentication system. The results of the fusion between iris and fingerprint authentication were 99% accurate. Data from Multimedia Malaysia University (MMUI) and our own prepared database, the SGU-MB-1 dataset, were used to test the accuracy of the proposed system.</dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2016-10-31</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/1542</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2016.10.3.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 10 No. 3 (2016); 209-227</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/1542/1801</dc:relation>
</oai_dc:dc>