Radial Distortion in Face Images: Detection and Impact

TL;DR

This paper investigates the impact of radial distortion in face images on face recognition systems, proposing a detection model to improve image quality assessment and ensure reliable biometric verification.

Contribution

It introduces a novel radial distortion detection model formalized as a face image quality assessment algorithm, addressing a less studied distortion type in face recognition.

Findings

High detection accuracy of the proposed model.

Radial distortion significantly degrades FRS performance.

Insights into optimal use of distortion detection in operational systems.

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.