Impact of Occlusion Masks on Gender Classification from Iris Texture
Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Biometrics |
| Online Access: | http://dx.doi.org/10.1049/2024/8526857 |
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| _version_ | 1825199961595183104 |
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| author | Claudio Yáñez Juan E. Tapia Claudio A. Perez Christoph Busch |
| author_facet | Claudio Yáñez Juan E. Tapia Claudio A. Perez Christoph Busch |
| author_sort | Claudio Yáñez |
| collection | DOAJ |
| description | Gender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation. |
| format | Article |
| id | doaj-art-804272c5014a463fb86d5d07a509548d |
| institution | Kabale University |
| issn | 2047-4946 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-804272c5014a463fb86d5d07a509548d2025-02-08T00:00:09ZengWileyIET Biometrics2047-49462024-01-01202410.1049/2024/8526857Impact of Occlusion Masks on Gender Classification from Iris TextureClaudio Yáñez0Juan E. Tapia1Claudio A. Perez2Christoph Busch3Department of Electrical Engineering, and Advanced Mining Technology Centerda/sec-Biometrics and Internet Security Research GroupDepartment of Electrical Engineering, and Advanced Mining Technology Centerda/sec-Biometrics and Internet Security Research GroupGender classification on normalized iris images has been previously attempted with varying degrees of success. In these previous studies, it has been shown that occlusion masks may introduce gender information; occlusion masks are used in iris recognition to remove non-iris elements. When, the goal is to classify the gender using exclusively the iris texture, the presence of gender information in the masks may result in apparently higher accuracy, thereby not reflecting the actual gender information present in the iris. However, no measures have been taken to eliminate this information while preserving as much iris information as possible. We propose a novel method to assess the gender information present in the iris more accurately by eliminating gender information in the masks. This consists of pairing iris with similar masks and different gender, generating a paired mask using the OR operator, and applying this mask to the iris. Additionally, we manually fix iris segmentation errors to study their impact on the gender classification. Our results show that occlusion masks can account for 6.92% of the gender classification accuracy on average. Therefore, works aiming to perform gender classification using the iris texture from normalized iris images should eliminate this correlation.http://dx.doi.org/10.1049/2024/8526857 |
| spellingShingle | Claudio Yáñez Juan E. Tapia Claudio A. Perez Christoph Busch Impact of Occlusion Masks on Gender Classification from Iris Texture IET Biometrics |
| title | Impact of Occlusion Masks on Gender Classification from Iris Texture |
| title_full | Impact of Occlusion Masks on Gender Classification from Iris Texture |
| title_fullStr | Impact of Occlusion Masks on Gender Classification from Iris Texture |
| title_full_unstemmed | Impact of Occlusion Masks on Gender Classification from Iris Texture |
| title_short | Impact of Occlusion Masks on Gender Classification from Iris Texture |
| title_sort | impact of occlusion masks on gender classification from iris texture |
| url | http://dx.doi.org/10.1049/2024/8526857 |
| work_keys_str_mv | AT claudioyanez impactofocclusionmasksongenderclassificationfromiristexture AT juanetapia impactofocclusionmasksongenderclassificationfromiristexture AT claudioaperez impactofocclusionmasksongenderclassificationfromiristexture AT christophbusch impactofocclusionmasksongenderclassificationfromiristexture |