An efficient Iris recognition technique using CNN and Vision Transformer
The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recogn...
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Semarak Ilmu Sdn Bhd
2023
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在线阅读: | http://irep.iium.edu.my/111175/2/111175_An%20efficient%20Iris%20recognition%20technique%20using%20CNN.pdf http://irep.iium.edu.my/111175/ https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/issue/view/208 https://doi.org/10.37934/araset.34.2.235245 |
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my.iium.irep.1111752024-03-07T06:28:56Z http://irep.iium.edu.my/111175/ An efficient Iris recognition technique using CNN and Vision Transformer Abdul Latif, Samihah Sidek, Khairul Azami Hassan Abdalla Hashim, Aisha TK7885 Computer engineering The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recognition compared to other modalities to combat identity abuse due to its ability to eliminate risk of collisions or false matches even when comparing large populations. The use of CNN is proven to provide high accuracy; however, this technology involves the need for a large dataset and higher computational cost. Therefore, this study uses a combined model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) in identifying and verifying an iris image. By using the proposed learning rate, it proves that the novel hybrid model is capable to achieve up to 93.66% accuracy in recognizing iris images. The cross-entropy loss function was implemented to reduce the loss and it was able to predict the class label more correctly. In addition, the model was thoroughly tested on three publicly available iris databases, achieving satisfactory iris recognition results. Furthermore, this model has the potential to be used in other biometrics such as face and retina recognitions. Semarak Ilmu Sdn Bhd 2023-12-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/111175/2/111175_An%20efficient%20Iris%20recognition%20technique%20using%20CNN.pdf Abdul Latif, Samihah and Sidek, Khairul Azami and Hassan Abdalla Hashim, Aisha (2023) An efficient Iris recognition technique using CNN and Vision Transformer. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (2). pp. 235-245. ISSN 2462-1943 https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/issue/view/208 https://doi.org/10.37934/araset.34.2.235245 |
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TK7885 Computer engineering Abdul Latif, Samihah Sidek, Khairul Azami Hassan Abdalla Hashim, Aisha An efficient Iris recognition technique using CNN and Vision Transformer |
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The usage of biometric identification has increased in recent years, with numerous public and commercial organizations incorporating biometric technologies into their infrastructures. One of the technologies is iris recognition which has been used as a biometric recognition compared to other modalities to combat identity abuse due to its ability to eliminate risk of collisions or false matches even when comparing large populations. The use of CNN is proven to provide high accuracy; however, this technology involves the need for a large dataset and higher computational cost. Therefore, this study uses a combined model of Convolutional Neural Network (CNN) and Vision Transformer (ViT) in identifying and verifying an iris image. By using the proposed learning rate, it proves that the novel hybrid model is capable to achieve up to 93.66% accuracy in recognizing iris images. The cross-entropy loss function was implemented to reduce the loss and it was able to predict the class label more correctly. In addition, the model was thoroughly tested on three publicly available iris databases, achieving satisfactory iris recognition results. Furthermore, this model has the potential to be used in other biometrics such as face and retina recognitions. |
format |
Article |
author |
Abdul Latif, Samihah Sidek, Khairul Azami Hassan Abdalla Hashim, Aisha |
author_facet |
Abdul Latif, Samihah Sidek, Khairul Azami Hassan Abdalla Hashim, Aisha |
author_sort |
Abdul Latif, Samihah |
title |
An efficient Iris recognition technique using CNN and Vision Transformer |
title_short |
An efficient Iris recognition technique using CNN and Vision Transformer |
title_full |
An efficient Iris recognition technique using CNN and Vision Transformer |
title_fullStr |
An efficient Iris recognition technique using CNN and Vision Transformer |
title_full_unstemmed |
An efficient Iris recognition technique using CNN and Vision Transformer |
title_sort |
efficient iris recognition technique using cnn and vision transformer |
publisher |
Semarak Ilmu Sdn Bhd |
publishDate |
2023 |
url |
http://irep.iium.edu.my/111175/2/111175_An%20efficient%20Iris%20recognition%20technique%20using%20CNN.pdf http://irep.iium.edu.my/111175/ https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/issue/view/208 https://doi.org/10.37934/araset.34.2.235245 |
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1793147978413768704 |
score |
13.154949 |