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 this https URL.
Abstract (translated)
面部图像质量评估（FIQA）旨在预测给定的人脸图像对于人脸识别系统（FR）的实用性。当前最先进的FIQA方法主要依赖于卷积神经网络（CNN），而视觉变压器（ViT）架构的潜力尚未被充分探索。这项工作提出了一种新的方法——ViT-FIQA，它通过扩展标准的专为面部识别优化的ViT骨干网来实现这一目标，并引入了一个可学习的质量标记(token)，用于预测任意给定人脸图像的标量实用性分数。 该可学习的质量标记与标准图像块标记(concatenated)结合在一起，整个序列通过ViT编码器处理，使用全局自我注意机制来汇集所有块之间的上下文信息。在骨干网的输出处，ViT-FIQA分支为两个头：（1）块标记通过全连接层传递，以学习具有边际惩罚softmax损失的人脸表示；（2）质量标记被馈送到回归头中，用于预测人脸样本的实用性。 针对多个挑战性的基准和多种人脸识别模型（包括CNN和ViT架构），进行了广泛的实验。这些实验证明了ViT-FIQA始终能达到顶级性能。该结果强调了基于变换器的架构在建模面部图像实用性方面的有效性，并突显出ViTs作为未来FIQA研究可扩展基础的巨大潜力。 原文链接：[这里插入链接]（请将这里的文本替换为实际的URL）
URL
https://arxiv.org/abs/2508.13957
