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Radial Distortion in Face Images: Detection and Impact

arXiv 2501.07179 ↗First posted 13 Jan 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 30 references that are in the map. First posted on arXiv 13 Jan 2025.

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14 of 30 references.

References · 14

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Learning an Animatable Detailed 3D Face Model from In-The-Wild Imagescited by 475
  3. 2022EMOCA: Emotion Driven Monocular Face Capture and Animationcited by 254
  4. 2020Face Image Quality Assessment: A Literature Surveycited by 79
  5. 2021CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiabilitycited by 68
  6. 2019FaceQnet: Quality Assessment for Face Recognition based on Deep Learningcited by 61
  7. 2020Biometric Quality: Review and Application to Face Recognition with FaceQnetcited by 41
  8. 2018FishEyeRecNet: A Multi-Context Collaborative Deep Network for Fisheye Image Rectificationcited by 32
  9. 2023Considerations on the Evaluation of Biometric Quality Assessment Algorithmscited by 14
  10. 2021FaceQvec: Vector Quality Assessment for Face Biometrics based on ISO Compliancecited by 8
  11. 2022Face Morphing Attacks and Face Image Quality: The Effect of Morphing and the Unsupervised Attack Detection by Qualitycited by 8
  12. 2020Fisheye Distortion Rectification from Deep Straight Linescited by 5
2 more
  1. 2023NeutrEx: A 3D Quality Component Measure on Facial Expression Neutralitycited by 4
  2. 2022Impact of Face Image Quality Estimation on Presentation Attack Detectioncited by 2

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