  Deep learning has received increasing interests in face recognition recently.
Large quantities of deep learning methods have been proposed to handle various
problems appeared in face recognition. Quite a lot deep methods claimed that
they have gained or even surpassed human-level face verification performance in
certain databases. As we know, face image quality poses a great challenge to
traditional face recognition methods, e.g. model-driven methods with
hand-crafted features. However, a little research focus on the impact of face
image quality on deep learning methods, and even human performance. Therefore,
we raise a question: Is face image quality still one of the challenges for deep
learning based face recognition, especially in unconstrained condition. Based
on this, we further investigate this problem on human level. In this paper, we
partition face images into three different quality sets to evaluate the
performance of deep learning methods on cross-quality face images in the wild,
and then design a human face verification experiment on these cross-quality
data. The result indicates that quality issue still needs to be studied
thoroughly in deep learning, human own better capability in building the
relations between different face images with large quality gaps, and saying
deep learning method surpasses human-level is too optimistic.
