Biometric Quality: Review and Application to Face Recognition with FaceQnet

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.

References in corpus (5)

• The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
• Quality Measures in Biometric Systems
• Quality-Based Conditional Processing in Multi-Biometrics: Application to Sensor Interoperability
• Algorithmic Discrimination: Formulation and Exploration in Deep Learning-based Face Biometrics
• Predicting Face Recognition Performance Using Image Quality

Cited by in corpus (10)

• Face Image Quality Assessment: A Literature Survey
• IFQA: Interpretable Face Quality Assessment
• Impact of Facial Tattoos and Paintings on Face Recognition Systems
• SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
• The Effect of Wearing a Face Mask on Face Image Quality
• SELM: Siamese Extreme Learning Machine with Application to Face Biometrics
• Radial Distortion in Face Images: Detection and Impact
• CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
• On the Applicability of Synthetic Data for Face Recognition
• Intelligent Frame Selection as a Privacy-Friendlier Alternative to Face Recognition