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/.

翻译：评估人脸图像质量对于确保人脸识别系统具有足够精度至关重要。最新的人脸质量标准化进展（ISO/IEC WD 29794-5）建议采用分量质量度量将人脸质量分解为独立因素，从而为操作人员提供有价值的反馈以重新采集低质量图像。基于三维感知生成对抗网络的最新进展，我们提出一个名为"Syn-YawPitch"的新颖数据集，包含1000个身份在偏航角与俯仰角组合变化下的图像。利用该数据集，我们证明超过30度的俯仰角会对当前人脸识别系统的生物特征性能产生显著影响。此外，我们提出一个符合ISO/IEC WD 29794-5标准的轻量高效姿态质量预测器，并免费发布于https://github.com/datasciencegrimmer/Syn-YawPitch/。