  Face image quality is an important factor in facial recognition systems as
its verification and recognition accuracy is highly dependent on the quality of
image presented. Rejecting low quality images can significantly increase the
accuracy of any facial recognition system. In this project, a simple approach
is presented to train a deep convolutional neural network to perform end-to-end
face image quality assessment. The work is done in 2 stages : First, generation
of quality score label and secondly, training a deep convolutional neural
network in a supervised manner to predict quality score between 0 and 1. The
generation of quality labels is done by comparing the face image with a
template of best quality images and then evaluating the normalized score based
on the similarity.
