Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment

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 WD 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 1,000 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 efficient pose quality predictor that adheres to the standards of ISO/IEC WD 29794-5 and is freely available for use at https://github.com/datasciencegrimmer/Syn-YawPitch/.