ViT-FIQA: Assessing Face Image Quality using Vision Transformers

2025-08-19

AI Summary

This work systematically investigates the potential of Vision Transformers (ViTs) for Face Image Quality Assessment (FIQA), addressing the limitations of prior CNN-based methods that fail to jointly model global semantics and image utility. To this end, we propose a learnable Quality Token that participates in global self-attention alongside patch tokens, and introduce a dual-branch architecture that jointly optimizes FIQA and face recognition. The model employs a quality regression head and a fully connected representation head, trained end-to-end using margin-penalized Softmax loss. Extensive experiments on multiple mainstream benchmarks—under diverse face recognition backbones—demonstrate state-of-the-art performance, validating ViT’s superior representational capacity, generalization ability, and scalability for FIQA.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Assessing face image quality for recognition systems
• Exploring Vision Transformers for quality prediction
• Predicting utility scores via learnable quality tokens

Innovation

• Vision Transformer backbone for face quality
• Learnable quality token for utility prediction
• Dual-head output for recognition and regression