A Lightweight Ensemble-Based Face Image Quality Assessment Method with Correlation-Aware Loss
This work addresses the need for efficient and accurate FIQA for face recognition systems in real-world applications, representing an incremental improvement over existing methods.
The paper tackles the problem of face image quality assessment (FIQA) in uncontrolled environments by proposing a lightweight ensemble method that achieves high correlation scores (SRCC 0.9829, PLCC 0.9894) on a benchmark while maintaining computational efficiency.
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.