Face Image Quality Estimation on Presentation Attack Detection

Non-referential Face Image Quality Assessment (FIQA) methods have gained popularity as a pre-filtering step in Face Recognition (FR) systems. In most of them, the quality score is usually designed with face comparison in mind. However, a small amount of work has been done on measuring their impact and usefulness on Presentation Attack Detection (PAD). In this paper, we study the effect of quality assessment methods on filtering bona fide and attack samples, their impact on PAD systems, and how the performance of such systems is improved when training on a filtered (by quality) dataset. On a Vision Transformer PAD algorithm, a reduction of 20% of the training dataset by remoing lower-quality samples allowed us to improve the Bona fide Presentation Classification Error Rate (BPCER) by 3% in a cross-dataset test.