Weak Preprocessing Iris Feature Matching Based on Bipartite Graph
Iris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching....
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | IET Signal Processing |
| Online Access: | http://dx.doi.org/10.1049/sil2/2013549 |
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| _version_ | 1839639856065544192 |
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| author | Jin Zhang Kangwei Wang Rongrong Shi Feng Xie Qinghe Zheng Ruizhe Zhang Cheng Wu Yiming Wang |
| author_facet | Jin Zhang Kangwei Wang Rongrong Shi Feng Xie Qinghe Zheng Ruizhe Zhang Cheng Wu Yiming Wang |
| author_sort | Jin Zhang |
| collection | DOAJ |
| description | Iris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. In response to these limitations, we propose a weak preprocessing algorithm for iris recognition that effectively preserves both grayscale and structural information of the iris. This approach is highly adaptable to varying image resolutions by leveraging a multiscale structural information extraction framework. It demonstrates significant improvements, achieving a matching accuracy of 96.67% on our proprietary dataset and 90% on the CASIA-IrisV4 dataset. Compared to the Daugman and OsIris 4.0 algorithm using weak preprocessing schemes, our approach improves accuracy by 15.55% and reduces matching time by 16%. More importantly, this method presents a new idea that is different from traditional preprocessing methods with wider adaptability. It offers considerable potential for real-world applications in security, with promising prospects for further integration with deep learning techniques. |
| format | Article |
| id | doaj-art-cf45971d3a6b46c6b3de4aee0fb4baa7 |
| institution | Matheson Library |
| issn | 1751-9683 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj-art-cf45971d3a6b46c6b3de4aee0fb4baa72025-07-04T05:00:03ZengWileyIET Signal Processing1751-96832025-01-01202510.1049/sil2/2013549Weak Preprocessing Iris Feature Matching Based on Bipartite GraphJin Zhang0Kangwei Wang1Rongrong Shi2Feng Xie3Qinghe Zheng4Ruizhe Zhang5Cheng Wu6Yiming Wang7School of Rail TransportationSchool of Rail TransportationSchool of Rail TransportationSchool of Rail TransportationSchool of Intelligent EngineeringSchool of Rail TransportationSchool of Rail TransportationSchool of Rail TransportationIris recognition is widely regarded as one of the most reliable biometric identification technologies. Traditional methods, such as the Daugman algorithm typically normalize the annular iris region into a rectangular format during the preprocessing stage, followed by feature extraction and matching. However, these preprocessing steps often introduce distortions and struggle to adapt to multiresolution images, leading to inaccurate feature encoding. In response to these limitations, we propose a weak preprocessing algorithm for iris recognition that effectively preserves both grayscale and structural information of the iris. This approach is highly adaptable to varying image resolutions by leveraging a multiscale structural information extraction framework. It demonstrates significant improvements, achieving a matching accuracy of 96.67% on our proprietary dataset and 90% on the CASIA-IrisV4 dataset. Compared to the Daugman and OsIris 4.0 algorithm using weak preprocessing schemes, our approach improves accuracy by 15.55% and reduces matching time by 16%. More importantly, this method presents a new idea that is different from traditional preprocessing methods with wider adaptability. It offers considerable potential for real-world applications in security, with promising prospects for further integration with deep learning techniques.http://dx.doi.org/10.1049/sil2/2013549 |
| spellingShingle | Jin Zhang Kangwei Wang Rongrong Shi Feng Xie Qinghe Zheng Ruizhe Zhang Cheng Wu Yiming Wang Weak Preprocessing Iris Feature Matching Based on Bipartite Graph IET Signal Processing |
| title | Weak Preprocessing Iris Feature Matching Based on Bipartite Graph |
| title_full | Weak Preprocessing Iris Feature Matching Based on Bipartite Graph |
| title_fullStr | Weak Preprocessing Iris Feature Matching Based on Bipartite Graph |
| title_full_unstemmed | Weak Preprocessing Iris Feature Matching Based on Bipartite Graph |
| title_short | Weak Preprocessing Iris Feature Matching Based on Bipartite Graph |
| title_sort | weak preprocessing iris feature matching based on bipartite graph |
| url | http://dx.doi.org/10.1049/sil2/2013549 |
| work_keys_str_mv | AT jinzhang weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT kangweiwang weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT rongrongshi weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT fengxie weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT qinghezheng weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT ruizhezhang weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT chengwu weakpreprocessingirisfeaturematchingbasedonbipartitegraph AT yimingwang weakpreprocessingirisfeaturematchingbasedonbipartitegraph |