PreFIQs: Face Image Quality Is What Survives Pruning
Jan Niklas Kolf, Guray Ozgur, Andrea Atzori, \v{Z}iga Babnik, Vitomir \v{S}truc, Naser Damer, Fadi Boutros

TL;DR
PreFIQs is an unsupervised, training-free face image quality assessment method that uses model pruning to identify images with high utility for face recognition, validated across multiple benchmarks.
Contribution
It introduces a novel pruning-based FIQA framework grounded in the PIE hypothesis, providing a theoretical justification and achieving state-of-the-art results without training.
Findings
PreFIQs outperforms existing FIQA methods on several benchmarks.
Model sparsification effectively indicates face image utility.
The approach is computationally efficient and training-free.
Abstract
Face Image Quality Assessment (FIQA) evaluates the utility of a face image for automated face recognition (FR) systems. In this work, we propose PreFIQs, an unsupervised and training-free FIQA framework grounded in the Pruning Identified Exemplar (PIE) hypothesis. We hypothesize that low-utility face images rely disproportionately on fragile network parameters, resulting in larger geometric displacement of their embeddings under model sparsification. Accordingly, PreFIQs quantifies image utility as the Euclidean distance between L2-normalized embeddings extracted from a pre-trained FR model and its pruned counterpart. We provide a first-order theoretical justification via a Jacobian-vector product analysis, demonstrating that this empirical drift serves as a computationally efficient approximation of the exact geometric sensitivity of the latent embedding manifold. Extensive experiments…
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