Quality Matters: Boosting Face Presentation Attack Detection With Image Quality Metrics

Abstract

Face Presentation Attack Detection (PAD) is critical for enhancing the security of facial recognition systems against sophisticated attacks. This study explores the use of general Image Quality Assessment (IQA) methods in face PAD, offering an alternative strategy that deviates from traditional, face-specific PAD techniques. Our evaluation of eight widely-used IQA methods across four PAD databases is structured around three distinct experimental protocols. Preliminary findings indicate that the general IQA methods are not fully effective in differentiating between genuine and attack samples, highlighting the need for modification. Nonetheless, a notable enhancement in performance is observed following the re-training of these methods using PAD datasets, bringing their effectiveness in line with that of advanced traditional PAD methods. This study provides evidence for the potential of general IQA in bolstering the resilience of face recognition systems against presentation attacks.