<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to extract the corresponding information, identity authentication is achieved. In a variety of biometrics, iris relative to fingerprints, handwriting and face, belongs to the structure within the human eye, if you want to steal is very difficult, in order to improve the safety factor, so the iris is used for authentication to achieve iris recognition. This study is based on deep learning iris recognition matching, in order to be able to effectively improve the accuracy of iris recognition, experiments are carried out. Evaluation metrics are performed through Hamming distance to calculate the correct recognition rate to ensure that the iris information can be accurately represented. This study mainly uses the improved PCHIP-LMD algorithm and CNN algorithm, the LMD algorithm is more context-aware, has better generalization ability, flexibility and scalability, while the CNN algorithm has the advantages of local awareness, parameter sharing and automatic parameter learning. In this study, we compare the correct recognition rate of iris recognition between improved PCHIP-LMD and CNN algorithms and get the conclusion that the correct recognition rate of the improved PCHIP-LMD algorithm is only 78%, which is much smaller than that of the CNN algorithm which is 92%, and we get the conclusion that LMD algorithm is suitable for iris recognition with few samples, and it is more suitable to use CNN algorithm when the sample images are too many. CNN algorithm. With the development of technology, the application of iris recognition will be more and more, I believe that soon will be widely popularized in daily life and work.</p>
</abstract>

Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to extract the corresponding information, identity authentication is achieved. In a variety of biometrics, iris relative to fingerprints, handwriting and face, belongs to the structure within the human eye, if you want to steal is very difficult, in order to improve the safety factor, so the iris is used for authentication to achieve iris recognition. This study is based on deep learning iris recognition matching, in order to be able to effectively improve the accuracy of iris recognition, experiments are carried out. Evaluation metrics are performed through Hamming distance to calculate the correct recognition rate to ensure that the iris information can be accurately represented. This study mainly uses the improved PCHIP-LMD algorithm and CNN algorithm, the LMD algorithm is more context-aware, has better generalization ability, flexibility and scalability, while the CNN algorithm has the advantages of local awareness, parameter sharing and automatic parameter learning. In this study, we compare the correct recognition rate of iris recognition between improved PCHIP-LMD and CNN algorithms and get the conclusion that the correct recognition rate of the improved PCHIP-LMD algorithm is only 78%, which is much smaller than that of the CNN algorithm which is 92%, and we get the conclusion that LMD algorithm is suitable for iris recognition with few samples, and it is more suitable to use CNN algorithm when the sample images are too many. CNN algorithm. With the development of technology, the application of iris recognition will be more and more, I believe that soon will be widely popularized in daily life and work.

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Certain biological information or behavioral information of a person can achieve the effect of characterizing an individual, and by combining the computer to...

Test Methods	Training Set	Test Set	Number Of Correct Identifications	Recognition Rate % (Crr)
CNN	400	60	56	92
LMD	400	60	47	78

	Training Set	Test Set	Training Set	Test Set
Number of categories	50	8	45	6
Number of images/classes	20	20	10	10
Total number of images	342	58	342	58

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

Published Online: Mar 15, 2024

Page range: 35 - 45

DOI: https://doi.org/10.2478/ijanmc-2023-0064

Keywords
Iris Recognition, Deep Learning, Normalization, Feature Extraction, LMD, CNN

© 2023 Yufei Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Analysis of experimental data

Comparison of the content of the two datasets

Research on Iris Feature Extraction and Recognition Technology Based on Deep Learning

Published Online: Mar 15, 2024

Page range: 35 - 45

DOI: https://doi.org/10.2478/ijanmc-2023-0064

KeywordsIris Recognition, Deep Learning, Normalization, Feature Extraction, LMD, CNN

© 2023 Yufei Chen et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure. 1.

Figure. 2.

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Figure. 11.

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Figure. 14.

Figure. 15.

Analysis of experimental data

Comparison of the content of the two datasets

Keywords
Iris Recognition, Deep Learning, Normalization, Feature Extraction, LMD, CNN