Abstract
Face image quality plays a critical role in determining the accuracy and reliability of face verification systems, particularly in real-time screening applications such as surveillance, identity verification, and access control. Low-quality face images, often caused by factors such as motion blur, poor lighting conditions, occlusions, and extreme pose variations, significantly degrade the performance of face recognition models, leading to higher false rejection and false acceptance rates. In this work, we propose a lightweight yet effective framework for automatic face quality assessment, which aims to pre-filter low-quality face images before they are passed to the verification pipeline. Our approach utilises normalised facial landmarks in conjunction with a Random Forest Regression classifier to assess image quality, achieving an accuracy of 96.67\%. By integrating this quality assessment module into the face verification process, we observe a substantial improvement in performance, including a comfortable 99.7\% reduction in the false rejection rate and enhanced cosine similarity scores when paired with the ArcFace face verification model. To validate our approach, we have conducted experiments on a real-world dataset collected comprising over 600 subjects captured from CCTV footage in unconstrained environments within Dubai Police. Our results demonstrate that the proposed framework effectively mitigates the impact of poor-quality face images, outperforming existing face quality assessment techniques while maintaining computational efficiency. Moreover, the framework specifically addresses two critical challenges in real-time screening: variations in face resolution and pose deviations, both of which are prevalent in practical surveillance scenarios.
Abstract (translated)
面部图像质量在决定人脸识别系统的准确性和可靠性方面起着关键作用，尤其是在监控、身份验证和访问控制等实时筛选应用中。低质量的面部图像通常由运动模糊、光照条件差、遮挡以及极端姿态变化等因素引起，这会显著降低人脸识别模型的表现，导致更高的误拒率和误识率。在本研究中，我们提出了一种轻量级但有效的自动面部质量评估框架，旨在筛选出低质量的面部图像，然后再将其传递给身份验证流程。我们的方法利用标准化的面部特征点与随机森林回归分类器结合来评估图像质量，达到了96.67%的准确率。通过将这种质量评估模块集成到人脸识别过程中，我们观察到了显著的性能提升，包括误拒率下降了99.7%，并与ArcFace人脸识别模型搭配使用时，余弦相似度评分也有所提高。 为了验证我们的方法，我们在一个包含超过600名受试者的真实世界数据集上进行了实验，该数据集由迪拜警察在不受控环境中从闭路电视录像中采集。我们实验证明，提出的框架有效减轻了低质量面部图像的影响，在保持计算效率的同时超越了现有的面部质量评估技术。此外，本框架特别解决了实时筛选中的两个关键挑战：面部分辨率的变化和姿态的偏移，这两种情况在实际监控场景中普遍存在。
URL
https://arxiv.org/abs/2507.15961
