The quality of face images significantly influences the performance of underlying face recognition algorithms. Face image quality assessment (FIQA) estimates the utility of the captured image in achieving reliable and accurate recognition performance. In this work, we propose a novel learning paradigm that learns internal network observations during the training process. Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability. This classifiability is measured based on the allocation of the training sample feature representation in angular space with respect to its class center and the nearest negative class center. We experimentally illustrate the correlation between the face image quality and the sample relative classifiability. As such property is only observable for the training dataset, we propose to learn this property from the training dataset and utilize it to predict the quality measure on unseen samples. This training is performed simultaneously while optimizing the class centers by an angular margin penalty-based softmax loss used for face recognition model training. Through extensive evaluation experiments on eight benchmarks and four face recognition models, we demonstrate the superiority of our proposed CR-FIQA over state-of-the-art (SOTA) FIQA algorithms.

翻译：人脸图像质量显著影响底层人脸识别算法的性能。人脸图像质量评估（FIQA）旨在评估所采集图像在实现可靠且准确识别性能中的效用。本文提出一种新颖的学习范式，能够在训练过程中学习网络内部观测特征。基于此，我们提出的CR-FIQA利用该范式通过预测样本的相对可分类性来评估人脸图像质量。该可分类性基于训练样本特征表示在角度空间中相对于其类别中心与最近负类别中心的分配情况来度量。我们通过实验揭示了人脸图像质量与样本相对可分类性之间的关联性。由于该特性仅对训练数据集可观测，本文提出从训练数据集中学习该特性，并利用其预测未见样本的质量度量。该训练过程与基于角度裕度惩罚的softmax损失（用于人脸识别模型训练）优化类别中心同步进行。通过在八个基准数据集和四种人脸识别模型上的广泛评估实验，我们证明了所提出的CR-FIQA相较于当前最优（SOTA）FIQA算法的优越性。