ViT-FIQA: Assessing Face Image Quality using Vision Transformers

• URL: http://arxiv.org/abs/2508.13957v3
• Date: Thu, 21 Aug 2025 23:57:34 GMT
• Title: ViT-FIQA: Assessing Face Image Quality using Vision Transformers
• Authors: Andrea Atzori, Fadi Boutros, Naser Damer,
• Abstract summary: Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems.<n>ViT-FIQA is a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token.<n>Experiments on challenging benchmarks and several FR models, including both CNN- and ViT-based architectures, demonstrate that ViT-FIQA consistently achieves top-tier performance.
• Score: 18.848638277203616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Face Image Quality Assessment (FIQA) aims to predict the utility of a face image for face recognition (FR) systems. State-of-the-art FIQA methods mainly rely on convolutional neural networks (CNNs), leaving the potential of Vision Transformer (ViT) architectures underexplored. This work proposes ViT-FIQA, a novel approach that extends standard ViT backbones, originally optimized for FR, through a learnable quality token designed to predict a scalar utility score for any given face image. The learnable quality token is concatenated with the standard image patch tokens, and the whole sequence is processed via global self-attention by the ViT encoders to aggregate contextual information across all patches. At the output of the backbone, ViT-FIQA branches into two heads: (1) the patch tokens are passed through a fully connected layer to learn discriminative face representations via a margin-penalty softmax loss, and (2) the quality token is fed into a regression head to learn to predict the face sample's utility. Extensive experiments on challenging benchmarks and several FR models, including both CNN- and ViT-based architectures, demonstrate that ViT-FIQA consistently achieves top-tier performance. These results underscore the effectiveness of transformer-based architectures in modeling face image utility and highlight the potential of ViTs as a scalable foundation for future FIQA research https://cutt.ly/irHlzXUC.

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