  In this paper we develop FaceQgen, a No-Reference Quality Assessment approach
for face images based on a Generative Adversarial Network that generates a
scalar quality measure related with the face recognition accuracy. FaceQgen
does not require labelled quality measures for training. It is trained from
scratch using the SCface database. FaceQgen applies image restoration to a face
image of unknown quality, transforming it into a canonical high quality image,
i.e., frontal pose, homogeneous background, etc. The quality estimation is
built as the similarity between the original and the restored images, since low
quality images experience bigger changes due to restoration. We compare three
different numerical quality measures: a) the MSE between the original and the
restored images, b) their SSIM, and c) the output score of the Discriminator of
the GAN. The results demonstrate that FaceQgen's quality measures are good
estimators of face recognition accuracy. Our experiments include a comparison
with other quality assessment methods designed for faces and for general
images, in order to position FaceQgen in the state of the art. This comparison
shows that, even though FaceQgen does not surpass the best existing face
quality assessment methods in terms of face recognition accuracy prediction, it
achieves good enough results to demonstrate the potential of semi-supervised
learning approaches for quality estimation (in particular, data-driven learning
based on a single high quality image per subject), having the capacity to
improve its performance in the future with adequate refinement of the model and
the significant advantage over competing methods of not needing quality labels
for its development. This makes FaceQgen flexible and scalable without
expensive data curation.
