监控面部图像质量评估：一个多维数据集和轻量模型
Surveillance Facial Image Quality Assessment: A Multi-dimensional Dataset and Lightweight Model
AI总结：
本文提出SFIQA-Bench和SFIQA-Assessor，针对监控场景中的质量与保真度问题，通过多任务模型实现高质量的面部图像评估。
摘要：
监控面部图像通常在非受控条件下捕获，导致由于低分辨率、运动模糊、遮挡和照明差等因素而出现严重的质量退化。尽管最近应用于监控摄像头的面部修复技术可以显著提高视觉质量，但它们往往牺牲了保真度（即身份保持特征），这直接与监控图像的主要目标——可靠的识别人脸——相冲突。现有的面部图像质量评估（FIQA）主要关注视觉质量和识别导向的评估，因此未能同时解决视觉质量和保真度，这对于监控应用至关重要。为弥补这一差距，我们提出了对监控面部图像质量评估（SFIQA）的首次综合性研究，针对监控场景固有的挑战。具体而言，我们首先构建了SFIQA-Bench，一个用于监控面部图像的多维质量评估基准，其中包括由三种广泛部署的监控摄像头在真实场景中捕获的5,004张监控面部图像。通过主观实验收集了六个维度的质量评分，包括噪声、清晰度、色彩丰富度、对比度、保真度和整体质量，涵盖了SFIQA的关键方面。此外，我们提出了SFIQA-Assessor，一个轻量级的多任务FIQA模型，通过跨视图特征交互共同利用互补的面部视图，并采用可学习的任务标记来指导多个质量维度的统一回归。在所提出的数据集上的实验结果表明，我们的方法在与最先进的通用图像质量评估（IQA）和FIQA方法相比，取得了最佳性能，验证了其在真实监控应用中的有效性。
英文摘要：
Surveillance facial images are often captured under unconstrained conditions, resulting in severe quality degradation due to factors such as low resolution, motion blur, occlusion, and poor lighting. Although recent face restoration techniques applied to surveillance cameras can significantly enhance visual quality, they often compromise fidelity (i.e., identity-preserving features), which directly conflicts with the primary objective of surveillance images -- reliable identity verification. Existing facial image quality assessment (FIQA) predominantly focus on either visual quality or recognition-oriented evaluation, thereby failing to jointly address visual quality and fidelity, which are critical for surveillance applications. To bridge this gap, we propose the first comprehensive study on surveillance facial image quality assessment (SFIQA), targeting the unique challenges inherent to surveillance scenarios. Specifically, we first construct SFIQA-Bench, a multi-dimensional quality assessment benchmark for surveillance facial images, which consists of 5,004 surveillance facial images captured by three widely deployed surveillance cameras in real-world scenarios. A subjective experiment is conducted to collect six dimensional quality ratings, including noise, sharpness, colorfulness, contrast, fidelity and overall quality, covering the key aspects of SFIQA. Furthermore, we propose SFIQA-Assessor, a lightweight multi-task FIQA model that jointly exploits complementary facial views through cross-view feature interaction, and employs learnable task tokens to guide the unified regression of multiple quality dimensions. The experiment results on the proposed dataset show that our method achieves the best performance compared with the state-of-the-art general image quality assessment (IQA) and FIQA methods, validating its effectiveness for real-world surveillance applications.
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