Abstract
Face Image Quality Assessment (FIQA) estimates the utility of face images for automated face recognition (FR) systems. We propose in this work a novel approach to assess the quality of face images based on inspecting the required changes in the pre-trained FR model weights to minimize differences between testing samples and the distribution of the FR training dataset. To achieve that, we propose quantifying the discrepancy in Batch Normalization statistics (BNS), including mean and variance, between those recorded during FR training and those obtained by processing testing samples through the pretrained FR model. We then generate gradient magnitudes of pretrained FR weights by backpropagating the BNS through the pretrained model. The cumulative absolute sum of these gradient magnitudes serves as the FIQ for our approach. Through comprehensive experimentation, we demonstrate the effectiveness of our training-free and quality labeling-free approach, achieving competitive performance to recent state-of-theart FIQA approaches without relying on quality labeling, the need to train regression networks, specialized architectures, or designing and optimizing specific loss functions.
Abstract (translated)
面部图像质量评估（FIQA）估计面部图像对自动面部识别（FR）系统的利用率。在本文中，我们提出了一种新方法来评估面部图像的质量，即根据检查预训练FR模型权重所需的更改来最小化测试样本与FR训练数据分布之间的差异。为了实现这一目标，我们提出计算在FR训练期间记录的BNS之间差异的方差，包括均值和方差，以及通过预训练FR模型处理测试样本获得的BNS之间的差异。然后，通过反向传播算法计算预训练FR权重的梯度大小。这些梯度大小的累积绝对和作为FIQ。通过全面的实验，我们证明了我们无需训练和免费的质量标注方法的有效性，实现了与最近 state-of-the-art FIQA 方法竞争的性能，而无需依赖质量标注。我们证明了不需要训练回归网络、专用架构或设计并优化特定的损失函数。
URL
https://arxiv.org/abs/2404.12203
