Radial Distortion in Face Images: Detection and Impact

2024-09-15

2024 IEEE International Joint Conference on Biometrics (IJCB)

AI Summary

This study addresses the prevalent radial distortion (fisheye effect) in smartphone-based ID photo capture, formally framing it as a facial image quality assessment (FIQA) problem and quantitatively characterizing how distortion type and severity degrade face recognition performance. We propose a deep joint model—combining binary classification and regression—that integrates geometric priors with facial structural constraints, trained using multi-scale synthetic and real-world distorted data augmentation. Our distortion detection model achieves an AUC of 0.987. Experiments demonstrate that moderate-to-severe distortion increases false rejection rates of mainstream recognition systems by up to 42%. Furthermore, we design a distortion-aware quality gating mechanism that significantly enhances recognition robustness and usability in online identity verification scenarios.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Facial Recognition
• Radial Distortion
• Selfie Photography

Innovation

• Radial Distortion Detection
• Facial Recognition Accuracy
• Fisheye Lens Effect