Radial Distortion in Face Images: Detection and Impact
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910782087757824 |
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| author | Kabbani, Wassim Pessot, Tristan Le Raja, Kiran Ramachandra, Raghavendra Busch, Christoph |
| author_facet | Kabbani, Wassim Pessot, Tristan Le Raja, Kiran Ramachandra, Raghavendra Busch, Christoph |
| contents | Acquiring face images of sufficiently high quality is important for online ID and travel document issuance applications using face recognition systems (FRS). Low-quality, manipulated (intentionally or unintentionally), or distorted images degrade the FRS performance and facilitate documents' misuse. Securing quality for enrolment images, especially in the unsupervised self-enrolment scenario via a smartphone, becomes important to assure FRS performance. In this work, we focus on the less studied area of radial distortion (a.k.a., the fish-eye effect) in face images and its impact on FRS performance. We introduce an effective radial distortion detection model that can detect and flag radial distortion in the enrolment scenario. We formalize the detection model as a face image quality assessment (FIQA) algorithm and provide a careful inspection of the effect of radial distortion on FRS performance. Evaluation results show excellent detection results for the proposed models, and the study on the impact on FRS uncovers valuable insights into how to best use these models in operational systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07179 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Radial Distortion in Face Images: Detection and Impact Kabbani, Wassim Pessot, Tristan Le Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision and Pattern Recognition Acquiring face images of sufficiently high quality is important for online ID and travel document issuance applications using face recognition systems (FRS). Low-quality, manipulated (intentionally or unintentionally), or distorted images degrade the FRS performance and facilitate documents' misuse. Securing quality for enrolment images, especially in the unsupervised self-enrolment scenario via a smartphone, becomes important to assure FRS performance. In this work, we focus on the less studied area of radial distortion (a.k.a., the fish-eye effect) in face images and its impact on FRS performance. We introduce an effective radial distortion detection model that can detect and flag radial distortion in the enrolment scenario. We formalize the detection model as a face image quality assessment (FIQA) algorithm and provide a careful inspection of the effect of radial distortion on FRS performance. Evaluation results show excellent detection results for the proposed models, and the study on the impact on FRS uncovers valuable insights into how to best use these models in operational systems. |
| title | Radial Distortion in Face Images: Detection and Impact |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.07179 |