Face quality assessment is important to improve the performance of face recognition system. For instance, it is required to
select images of good quality to improve recognition rate for the person of interest. Current methods mostly depend on
traditional image assessment, which use prior knowledge of human vision system. As a result, the quality score of face
images shows consistency with human vision perception but deviates from the processing procedure of a real face
recognition system. It is the fact that the state-of-art face recognition systems are all built on deep neural networks.
Naturally, it is expected to propose an efficient quality scoring method of face images, which should show high consistency
with the recognition rate of face images from current face recognition systems. This paper proposes a non-reference face
image assessment algorithm based on the deep features, which is capable of predicting the recognition rate of face images.
The proposed face image assessment algorithm provides a promising tool to filter out the good input images for the real
face recognition system to achieve high recognition rate.