  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.
