UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS

The advantages of the ears as a means of identification over other biometric modalities provided an avenue for researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presented a deep learning pipeline for unconstrained ear recognition using a Transform...

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Autor principal:	Marwin Alejo
Formato:	article
Lenguaje:	EN
Publicado:	Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT) 2021
Materias:	deep learning neural network transformers vision transformer data-efficient image transformers ear recognition Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/2d69ca20c56f4b8fae1155a925ccf202
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id	oai:doaj.org-article:2d69ca20c56f4b8fae1155a925ccf202
record_format	dspace
spelling	oai:doaj.org-article:2d69ca20c56f4b8fae1155a925ccf2022021-12-03T07:32:06ZUNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS2413-935110.5455/jjcit.71-1627981530https://doaj.org/article/2d69ca20c56f4b8fae1155a925ccf2022021-12-01T00:00:00Zhttp://www.ejmanager.com/fulltextpdf.php?mno=105768https://doaj.org/toc/2413-9351The advantages of the ears as a means of identification over other biometric modalities provided an avenue for researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presented a deep learning pipeline for unconstrained ear recognition using a Transformer neural network: Vision Transformer (ViT) and Data-efficient image Transformers (DeiT). The ViT-Ear and DeiT-Ear models of this study achieved recognition accuracy comparable or more significant than the results of state-of-the-art CNN-based methods and other deep learning algorithms. This study also determined that the performance of Vision Transformer and Data-efficient image Transformer models work better than ResNets without using exhaustive data augmentation processes. Moreover, this study observed that the performance of ViT-Ear is nearly similar to other ViT-based biometric studies. [JJCIT 2021; 7(4.000): 326-336]Marwin AlejoScientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)articledeep learningneural networktransformersvision transformerdata-efficient image transformersear recognitionInformation technologyT58.5-58.64Electronic computers. Computer scienceQA75.5-76.95ENJordanian Journal of Computers and Information Technology , Vol 7, Iss 4, Pp 326-336 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning neural network transformers vision transformer data-efficient image transformers ear recognition Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95
spellingShingle	deep learning neural network transformers vision transformer data-efficient image transformers ear recognition Information technology T58.5-58.64 Electronic computers. Computer science QA75.5-76.95 Marwin Alejo UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
description	The advantages of the ears as a means of identification over other biometric modalities provided an avenue for researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presented a deep learning pipeline for unconstrained ear recognition using a Transformer neural network: Vision Transformer (ViT) and Data-efficient image Transformers (DeiT). The ViT-Ear and DeiT-Ear models of this study achieved recognition accuracy comparable or more significant than the results of state-of-the-art CNN-based methods and other deep learning algorithms. This study also determined that the performance of Vision Transformer and Data-efficient image Transformer models work better than ResNets without using exhaustive data augmentation processes. Moreover, this study observed that the performance of ViT-Ear is nearly similar to other ViT-based biometric studies. [JJCIT 2021; 7(4.000): 326-336]
format	article
author	Marwin Alejo
author_facet	Marwin Alejo
author_sort	Marwin Alejo
title	UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
title_short	UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
title_full	UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
title_fullStr	UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
title_full_unstemmed	UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
title_sort	unconstrained ear recognition using transformers
publisher	Scientific Research Support Fund of Jordan (SRSF) and Princess Sumaya University for Technology (PSUT)
publishDate	2021
url	https://doaj.org/article/2d69ca20c56f4b8fae1155a925ccf202
work_keys_str_mv	AT marwinalejo unconstrainedearrecognitionusingtransformers
_version_	1718373801180266496

UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS

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