A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss

• URL: http://arxiv.org/abs/2509.10114v1
• Date: Fri, 12 Sep 2025 10:13:38 GMT
• Title: A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
• Authors: MohammadAli Hamidi, Hadi Amirpour, Luigi Atzori, Christian Timmerer,
• Abstract summary: Face image quality assessment (FIQA) plays a critical role in face recognition and verification systems.<n>We propose a lightweight and efficient method for FIQA, designed for the perceptual evaluation of face images in the wild.
• Score: 14.915614314380578
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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