  "The output of a computerised system can only be as accurate as the
information entered into it." This rather trivial statement is the basis behind
one of the driving concepts in biometric recognition: biometric quality.
Quality is nowadays widely regarded as the number one factor responsible for
the good or bad performance of automated biometric systems. It refers to the
ability of a biometric sample to be used for recognition purposes and produce
consistent, accurate, and reliable results. Such a subjective term is
objectively estimated by the so-called biometric quality metrics. These
algorithms play nowadays a pivotal role in the correct functioning of systems,
providing feedback to the users and working as invaluable audit tools. In spite
of their unanimously accepted relevance, some of the most used and deployed
biometric characteristics are lacking behind in the development of these
methods. This is the case of face recognition. After a gentle introduction to
the general topic of biometric quality and a review of past efforts in face
quality metrics, in the present work, we address the need for better face
quality metrics by developing FaceQnet. FaceQnet is a novel open-source face
quality assessment tool, inspired and powered by deep learning technology,
which assigns a scalar quality measure to facial images, as prediction of their
recognition accuracy. Two versions of FaceQnet have been thoroughly evaluated
both in this work and also independently by NIST, showing the soundness of the
approach and its competitiveness with respect to current state-of-the-art
metrics. Even though our work is presented here particularly in the framework
of face biometrics, the proposed methodology for building a fully automated
quality metric can be very useful and easily adapted to other artificial
intelligence tasks.
