Staff View: Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-varian...

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Main Authors:	Irwandi Hipni, Mohamad Hipiny, Hamimah, Ujir, Aazani, Mujahid, Nurhartini Kamalia, Yahya
Format:	Article
Language:	English
Published:	ITB Journal Publisher 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://ir.unimas.my/id/eprint/23328/1/Towards%20Automated%20Biometric%20Identification%20of%20Sea%20Turtles%20%28Chelonia%20mydas%29%20-%20Copy.pdf http://ir.unimas.my/id/eprint/23328/ http://journals.itb.ac.id/index.php/jictra/index
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id	my.unimas.ir.23328
record_format	eprints
spelling	my.unimas.ir.233282023-03-23T07:33:51Z http://ir.unimas.my/id/eprint/23328/ Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas) Irwandi Hipni, Mohamad Hipiny Hamimah, Ujir Aazani, Mujahid Nurhartini Kamalia, Yahya QA75 Electronic computers. Computer science Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%. ITB Journal Publisher 2018-12 Article PeerReviewed text en http://ir.unimas.my/id/eprint/23328/1/Towards%20Automated%20Biometric%20Identification%20of%20Sea%20Turtles%20%28Chelonia%20mydas%29%20-%20Copy.pdf Irwandi Hipni, Mohamad Hipiny and Hamimah, Ujir and Aazani, Mujahid and Nurhartini Kamalia, Yahya (2018) Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas). Journal of ICT Research and Applications, 12 (3). pp. 256-266. ISSN 2337-5787 http://journals.itb.ac.id/index.php/jictra/index DOI:10.5614/itbj.ict.res.appl.2018.12.3.4.
institution	Universiti Malaysia Sarawak
building	Centre for Academic Information Services (CAIS)
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Sarawak
content_source	UNIMAS Institutional Repository
url_provider	http://ir.unimas.my/
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Irwandi Hipni, Mohamad Hipiny Hamimah, Ujir Aazani, Mujahid Nurhartini Kamalia, Yahya Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
description	Passive biometric identification enables wildlife monitoring with minimal disturbance. Using a motion-activated camera placed at an elevated position and facing downwards, we collected images of sea turtle carapace, each belonging to one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust image descriptors from these images, enabling indexing and retrieval. In this work, we presented several classification results of sea turtle carapaces using the learned image descriptors. We found that a template-based descriptor, i.e., Histogram of Oriented Gradients (HOG) performed exceedingly better during classification than keypoint-based descriptors. For our dataset, a high-dimensional descriptor is a must due to the minimal gradient and color information inside the carapace images. Using HOG, we obtained an average classification accuracy of 65%.
format	Article
author	Irwandi Hipni, Mohamad Hipiny Hamimah, Ujir Aazani, Mujahid Nurhartini Kamalia, Yahya
author_facet	Irwandi Hipni, Mohamad Hipiny Hamimah, Ujir Aazani, Mujahid Nurhartini Kamalia, Yahya
author_sort	Irwandi Hipni, Mohamad Hipiny
title	Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
title_short	Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
title_full	Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
title_fullStr	Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
title_full_unstemmed	Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)
title_sort	towards automated biometric identification of sea turtles (chelonia mydas)
publisher	ITB Journal Publisher
publishDate	2018
url	http://ir.unimas.my/id/eprint/23328/1/Towards%20Automated%20Biometric%20Identification%20of%20Sea%20Turtles%20%28Chelonia%20mydas%29%20-%20Copy.pdf http://ir.unimas.my/id/eprint/23328/ http://journals.itb.ac.id/index.php/jictra/index
_version_	1761623566051180544
score	13.211869

Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

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