  As the deep learning makes big progresses in still-image face recognition,
unconstrained video face recognition is still a challenging task due to low
quality face images caused by pose, blur, occlusion, illumination etc. In this
paper we propose a network for face recognition which gives an explicit and
quantitative quality score at the same time when a feature vector is extracted.
To our knowledge this is the first network that implements these two functions
in one network online. This network is very simple by adding a quality network
branch to the baseline network of face recognition. It does not require
training datasets with annotated face quality labels. We evaluate this network
on both still-image face datasets and video face datasets and achieve the
state-of-the-art performance in many cases. This network enables a lot of
applications where an explicit face quality scpre is used. We demonstrate three
applications of the explicit face quality, one of which is a progressive
feature aggregation scheme in online video face recognition. We design an
experiment to prove the benefits of using the face quality in this application.
Code will be available at \url{https://github.com/deepcam-cn/facequality}.
