  Evaluating the quality of facial images is essential for operating face
recognition systems with sufficient accuracy. The recent advances in face
quality standardisation (ISO/IEC CD3 29794-5) recommend the usage of component
quality measures for breaking down face quality into its individual factors,
hence providing valuable feedback for operators to re-capture low-quality
images. In light of recent advances in 3D-aware generative adversarial
networks, we propose a novel dataset, Syn-YawPitch, comprising 1000 identities
with varying yaw-pitch angle combinations. Utilizing this dataset, we
demonstrate that pitch angles beyond 30 degrees have a significant impact on
the biometric performance of current face recognition systems. Furthermore, we
propose a lightweight and explainable pose quality predictor that adheres to
the draft international standard of ISO/IEC CD3 29794-5 and benchmark it
against state-of-the-art face image quality assessment algorithms
