An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition

Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First,...

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Autores principales:	Dong Zou, Jianbing Feng, Zhixin He, Liping Liu, Meijun Zhao, Lizhong Zheng
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/6bfb74c966104a9bbf5e54f84a5d03e5
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spelling	oai:doaj.org-article:6bfb74c966104a9bbf5e54f84a5d03e52021-11-08T02:36:31ZAn OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition1563-514710.1155/2021/3412060https://doaj.org/article/6bfb74c966104a9bbf5e54f84a5d03e52021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3412060https://doaj.org/toc/1563-5147Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First, binarized threshold segmentation is used to locate the pupil and the pupil boundary is obtained; then, the Hough transform is applied to locate the outer edge of the iris; according to the located pupil and iris, the iris area image is obtained through image segmentation; finally, the iris image is normalized to adjust each original image to the same size and corresponding position, so as to eliminate the influence of translation, scaling, and rotation on iris recognition. Second, the normalized iris images are both input into the octave convolution module and attention module. The octave convolution module is used to extract the shape and contour features of the iris by decomposing the feature map into high and low frequencies. The attention module is applied to extract the color and texture characteristics of the iris. Finally, the two feature maps are concatenated and produce a distribution of output classes. Experimental results show that the proposed OCaNet model is significantly more accurate.Dong ZouJianbing FengZhixin HeLiping LiuMeijun ZhaoLizhong ZhengHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Dong Zou Jianbing Feng Zhixin He Liping Liu Meijun Zhao Lizhong Zheng An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
description	Iris recognition refers to identifying individuals based on iris patterns, which have been widely used in security systems, such as subway security and access control attendance, because everyone has a unique iris shape. In the study, we propose an OCaNet model for the iris recognition task. First, binarized threshold segmentation is used to locate the pupil and the pupil boundary is obtained; then, the Hough transform is applied to locate the outer edge of the iris; according to the located pupil and iris, the iris area image is obtained through image segmentation; finally, the iris image is normalized to adjust each original image to the same size and corresponding position, so as to eliminate the influence of translation, scaling, and rotation on iris recognition. Second, the normalized iris images are both input into the octave convolution module and attention module. The octave convolution module is used to extract the shape and contour features of the iris by decomposing the feature map into high and low frequencies. The attention module is applied to extract the color and texture characteristics of the iris. Finally, the two feature maps are concatenated and produce a distribution of output classes. Experimental results show that the proposed OCaNet model is significantly more accurate.
format	article
author	Dong Zou Jianbing Feng Zhixin He Liping Liu Meijun Zhao Lizhong Zheng
author_facet	Dong Zou Jianbing Feng Zhixin He Liping Liu Meijun Zhao Lizhong Zheng
author_sort	Dong Zou
title	An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
title_short	An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
title_full	An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
title_fullStr	An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
title_full_unstemmed	An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition
title_sort	ocanet model based on octave convolution and attention mechanism for iris recognition
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/6bfb74c966104a9bbf5e54f84a5d03e5
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An OCaNet Model Based on Octave Convolution and Attention Mechanism for Iris Recognition

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