Abstract
Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems, especially in uncontrolled, real-world environments. Although several methods have been proposed, general-purpose no-reference image quality assessment techniques often fail to capture face-specific degradations. Meanwhile, state-of-the-art FIQA models tend to be computationally intensive, limiting their practical applicability. We propose a lightweight and efficient method for FIQA, designed for the perceptual evaluation of face images in the wild. Our approach integrates an ensemble of two compact convolutional neural networks, MobileNetV3-Small and ShuffleNetV2, with prediction-level fusion via simple averaging. To enhance alignment with human perceptual judgments, we employ a correlation-aware loss (MSECorrLoss), combining mean squared error (MSE) with a Pearson correlation regularizer. Our method achieves a strong balance between accuracy and computational cost, making it suitable for real-world deployment. Experiments on the VQualA FIQA benchmark demonstrate that our model achieves a Spearman rank correlation coefficient (SRCC) of 0.9829 and a Pearson linear correlation coefficient (PLCC) of 0.9894, remaining within competition efficiency constraints.
Abstract (translated)
人脸图像质量评估（FIQA）在人脸识别和验证系统中扮演着关键角色，特别是在不受控的现实世界环境中。尽管已经提出了几种方法，但通用的无参考图像质量评估技术往往无法捕捉到特定于人脸的退化情况。与此同时，最先进的FIQA模型通常计算密集度高，限制了其实用性。我们提出了一种轻量且高效的FIQA方法，旨在对野外的人脸图像进行感知评价。我们的方法结合了一个由两个紧凑型卷积神经网络（MobileNetV3-Small和ShuffleNetV2）组成的集成，并通过简单的平均在预测层面融合。为了增强与人类感知判断的一致性，我们采用了一种相关感知损失（MSECorrLoss），该损失将均方误差（MSE）与皮尔逊相关正则化相结合。我们的方法实现了准确性和计算成本之间的强大平衡，使其适合于实际部署。在VQualA FIQA基准测试上的实验表明，我们的模型达到了斯皮尔曼等级相关系数（SRCC）0.9829和皮尔森线性相关系数（PLCC）0.9894的性能水平，并且仍然符合竞赛效率约束条件。
URL
https://arxiv.org/abs/2509.10114
