Face image quality is an important factor to enable high performance face
recognition systems. Face quality assessment aims at estimating the suitability
of a face image for recognition. Previous work proposed supervised solutions
that require artificially or human labelled quality values. However, both
labelling mechanisms are error-prone as they do not rely on a clear definition
of quality and may not know the best characteristics for the utilized face
recognition system. Avoiding the use of inaccurate quality labels, we proposed
a novel concept to measure face quality based on an arbitrary face recognition
model. By determining the embedding variations generated from random
subnetworks of a face model, the robustness of a sample representation and
thus, its quality is estimated. The experiments are conducted in a
cross-database evaluation setting on three publicly available databases. We
compare our proposed solution on two face embeddings against six
state-of-the-art approaches from academia and industry. The results show that
our unsupervised solution outperforms all other approaches in the majority of
the investigated scenarios. In contrast to previous works, the proposed
solution shows a stable performance over all scenarios. Utilizing the deployed
face recognition model for our face quality assessment methodology avoids the
training phase completely and further outperforms all baseline approaches by a
large margin. Our solution can be easily integrated into current face
recognition systems and can be modified to other tasks beyond face recognition.

本文提出了一种基于随机子网络的人脸图像质量评估方法，使用嵌入变化来估计样本表示的健壮性和相应的图像质量，并在三个公开数据库上进行了交叉数据库评估。实验表明，此方法超过了其他学术和工业界的六种方法，表现出稳定的性能。