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Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image

Scientific Journal of Informatics · Vol. 13 No. 1 (2026) · hlm. 41–46

DOI
10.15294/sji.v13i1.37383
ISSN
2407-7658, 2460-0040
Terbit
2026-02-01
Penerbit
Universitas Negeri Semarang
Bahasa
eng

Abstrak

Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction. Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies. Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%. Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.unnes.ac.id/journals/sji/oai, pertama di-harvest 6 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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">Robust Human Gait Recognition with Convolutional Neural Network based on Gait Energy Image</dc:title>
	<dc:creator xml:lang="en">Pratama, Fandy Indra </dc:creator>
	<dc:creator xml:lang="en">Akhmad Pandhu Wijaya</dc:creator>
	<dc:creator xml:lang="en">Gilar Pandu Annanto</dc:creator>
	<dc:creator xml:lang="en">Avira Budianita</dc:creator>
	<dc:creator xml:lang="en">Hairudin Farid Sunanda</dc:creator>
	<dc:subject xml:lang="en">Authentication</dc:subject>
	<dc:subject xml:lang="en">Biometrics</dc:subject>
	<dc:subject xml:lang="en">CNN</dc:subject>
	<dc:subject xml:lang="en">GEI</dc:subject>
	<dc:subject xml:lang="en">Gait Recognition</dc:subject>
	<dc:description xml:lang="en">Purpose: Human gait recognition is one of the developments in artificial intelligence technology. Gait recognition is a biometric recognition technique that uses no direct interaction with an object, allowing for identification of individuals based on their gait. However, this recognition faces challenges, including varying camera angles (00 - 1800), so this requires a more in-depth introduction.
Methods: Therefore, based on the references, this study proposes using the Gait Energy Image (GEI) and Convolutional Neural Network (CNN) features for in-depth extraction and recognition of each image in the Casia B Dataset, which is then compared with the results of previous studies.
Result: The results of this study, with the division of the Casia B Dataset 80% as training data and 20% as testing data and 11 camera angles between 00 - 1800 produced an accuracy rate of 99.48%.
Novelty: So the accuracy achieved with this deep learning technique exceeds that of previous research using conventional methods and this gait pattern recognition technique can be used to be implemented in a biometric recognition system based on human gait patterns.</dc:description>
	<dc:publisher xml:lang="en">Universitas Negeri Semarang</dc:publisher>
	<dc:date>2026-02-01</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">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.unnes.ac.id/journals/sji/article/view/37383</dc:identifier>
	<dc:identifier>10.15294/sji.v13i1.37383</dc:identifier>
	<dc:source xml:lang="en">Scientific Journal of Informatics; Vol. 13 No. 1: Regular Issue; 41-46</dc:source>
	<dc:source>2407-7658</dc:source>
	<dc:source>2460-0040</dc:source>
	<dc:source>10.15294/sji.v13i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.unnes.ac.id/journals/sji/article/view/37383/8427</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2026 Scientific Journal of Informatics</dc:rights>
</oai_dc:dc>