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

Biometric Quality: Review and Application to Face Recognition with FaceQnet