检测人脸图像径向畸变，提升身份认证系统可靠性
Radial Distortion in Face Images: Detection and Impact
- 构建可自监督检测径向畸变的算法模型
- 发现畸变显著降低人脸识别准确率
- 适用于手机自助注册场景的质量评估
在基于人脸识别的在线身份认证与证件发放应用中，获取高质量人脸图像至关重要。低质量、被篡改或存在畸变的图像会降低识别性能，增加证件滥用风险。尤其在通过智能手机进行无监督自助注册时，确保录入图像质量尤为关键。本文聚焦于较少研究的径向畸变（即鱼眼效应）及其对人脸识别系统性能的影响。提出一种有效的径向畸变检测模型，可在注册环节识别并标记畸变图像。将该模型形式化为一种人脸图像质量评估（FIQA）算法，并深入分析了径向畸变对人脸识别性能的影响。实验结果显示，所提模型检测效果优异，且关于畸变影响的研究为实际系统中模型的应用提供了重要洞察。
原文摘要 · Abstract (English)
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.
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