Biometric Quality: Review and Application to Face Recognition with FaceQnet

TL;DR

This paper reviews biometric quality concepts and introduces FaceQnet, a deep learning-based face quality assessment tool that predicts recognition accuracy, demonstrating its effectiveness and competitiveness with existing metrics.

Contribution

The paper presents FaceQnet, a novel open-source face quality metric using deep learning, and evaluates its performance against state-of-the-art methods, highlighting its accuracy and adaptability.

Findings

FaceQnet effectively predicts face recognition accuracy.

FaceQnet outperforms or matches current state-of-the-art metrics.

The methodology can be adapted to other AI tasks.

Abstract

"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.