Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition
In this work, we address the problem of cross-resolution face recognition, where a low-resolution probe face is compared against high-resolution gallery faces. To address this challenging problem, we investigate two approaches for bridging the quality gap between low-quality probe faces and high-qua...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10833634/ |
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| author | Klemen Grm Berk Kemal Ozata Alperen Kantarci Vitomir Struc Hazim Kemal Ekenel |
| author_facet | Klemen Grm Berk Kemal Ozata Alperen Kantarci Vitomir Struc Hazim Kemal Ekenel |
| author_sort | Klemen Grm |
| collection | DOAJ |
| description | In this work, we address the problem of cross-resolution face recognition, where a low-resolution probe face is compared against high-resolution gallery faces. To address this challenging problem, we investigate two approaches for bridging the quality gap between low-quality probe faces and high-quality gallery faces. The first approach focuses on degrading the quality of high-resolution gallery images to bring them closer to the quality of the probe images. The second approach involves enhancing the resolution of the probe images using face hallucination. Our experiments on the SCFace and DroneSURF datasets reveal that the success of face hallucination is highly dependent on the quality of the original images, since poor image quality can severely limit the effectiveness of the hallucination technique. Therefore, the selection of the appropriate face recognition method should consider the quality of the images. Additionally, our experiments also suggest that combining gallery degradation and face hallucination in a hybrid recognition scheme provides the best overall results for cross-resolution face recognition with relatively high-quality probe images, while the degradation process on its own is the more suitable option for low-quality probe images. Our results show that the combination of standard computer vision approaches such as degradation, super-resolution, feature fusion, and score fusion can be used to substantially improve performance on the task of low resolution face recognition using off-the-shelf face recognition models without re-training on the target domain. |
| format | Article |
| id | doaj-art-dd6f0d3386a94ef696f3dee17f6bd515 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dd6f0d3386a94ef696f3dee17f6bd5152025-01-21T00:01:14ZengIEEEIEEE Access2169-35362025-01-0113105421055810.1109/ACCESS.2025.352723610833634Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face RecognitionKlemen Grm0https://orcid.org/0000-0002-3637-8182Berk Kemal Ozata1Alperen Kantarci2Vitomir Struc3https://orcid.org/0000-0002-3385-5780Hazim Kemal Ekenel4https://orcid.org/0000-0003-3697-8548Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, SloveniaDepartment of Computer Engineering, Istanbul Technical University, Istanbul, TürkiyeDepartment of Computer Engineering, Istanbul Technical University, Istanbul, TürkiyeFaculty of Electrical Engineering, University of Ljubljana, Ljubljana, SloveniaDepartment of Computer Engineering, Istanbul Technical University, Istanbul, TürkiyeIn this work, we address the problem of cross-resolution face recognition, where a low-resolution probe face is compared against high-resolution gallery faces. To address this challenging problem, we investigate two approaches for bridging the quality gap between low-quality probe faces and high-quality gallery faces. The first approach focuses on degrading the quality of high-resolution gallery images to bring them closer to the quality of the probe images. The second approach involves enhancing the resolution of the probe images using face hallucination. Our experiments on the SCFace and DroneSURF datasets reveal that the success of face hallucination is highly dependent on the quality of the original images, since poor image quality can severely limit the effectiveness of the hallucination technique. Therefore, the selection of the appropriate face recognition method should consider the quality of the images. Additionally, our experiments also suggest that combining gallery degradation and face hallucination in a hybrid recognition scheme provides the best overall results for cross-resolution face recognition with relatively high-quality probe images, while the degradation process on its own is the more suitable option for low-quality probe images. Our results show that the combination of standard computer vision approaches such as degradation, super-resolution, feature fusion, and score fusion can be used to substantially improve performance on the task of low resolution face recognition using off-the-shelf face recognition models without re-training on the target domain.https://ieeexplore.ieee.org/document/10833634/Biometricsimage processingmachine learning |
| spellingShingle | Klemen Grm Berk Kemal Ozata Alperen Kantarci Vitomir Struc Hazim Kemal Ekenel Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition IEEE Access Biometrics image processing machine learning |
| title | Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition |
| title_full | Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition |
| title_fullStr | Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition |
| title_full_unstemmed | Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition |
| title_short | Degrade or Super-Resolve to Recognize? Bridging the Domain Gap for Cross-Resolution Face Recognition |
| title_sort | degrade or super resolve to recognize bridging the domain gap for cross resolution face recognition |
| topic | Biometrics image processing machine learning |
| url | https://ieeexplore.ieee.org/document/10833634/ |
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