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Jurnal SINTA 2

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Journal of ICT Research and Applications (JICTRA) · Vol. 12 No. 3 (2018) · pp. 256–266

DOI
10.5614/itbj.ict.res.appl.2018.12.3.4
ISSN
2338-5499, 2337-5787
Published
2018-12-31
Publisher
DRI - ITB
Language
eng

Abstract

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%.

Keywords

Collaboration network of the authors

4 authors · 6 co-authorship links · 3 across different organisations

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  • Universiti Malaysia Sarawak
  • Cardiff University
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles

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">Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)</dc:title>
	<dc:creator>Hipiny, Irwandi</dc:creator>
	<dc:creator>Ujir, Hamimah</dc:creator>
	<dc:creator>Mujahid, Aazani</dc:creator>
	<dc:creator>Yahya, Nurhartini Kamalia</dc:creator>
	<dc:subject xml:lang="en-US">content-based image retrieval</dc:subject>
	<dc:subject xml:lang="en-US">invariant feature descriptor</dc:subject>
	<dc:subject xml:lang="en-US">multimedia databases</dc:subject>
	<dc:subject xml:lang="en-US">template matching</dc:subject>
	<dc:subject xml:lang="en-US">visual animal biometrics.</dc:subject>
	<dc:description xml:lang="en-US">Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, images of sea turtle carapaces were collected, each belonging to one of sixteen Chelonia mydas juveniles. Then, co-variant and robust image descriptors from these images were learned, enabling indexing and retrieval. In this paper, several classification results of sea turtle carapaces using the learned image descriptors are presented. It was found that a template-based descriptor, i.e. Histogram of Oriented Gradients (HOG) performed much better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must because of the minimal gradient and color information in the carapace images. Using HOG, we obtained an average classification accuracy of 65%. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2018-12-31</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>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/6909</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.res.appl.2018.12.3.4</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 12 No. 3 (2018); 256-266</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/6909/3847</dc:relation>
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