Abstract
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 this https URL.
Abstract (translated)
评估面部图像的质量对于实现足够准确的面部识别系统至关重要。近期的面部质量标准化进展(ISO/IEC WD 29794-5)建议将面部质量分解成个体因素,并使用组件质量指标进行衡量,因此为操作员提供了有价值的反馈,以重新捕获低质量图像。考虑到3Daware生成对抗网络的最新进展,我们提出了一个独特的数据集,“Syn-YawPitch”,包括不同 Yaw-Pitch 角度的组合的1,000个身份。利用这个数据集,我们证明超过30度的 Yaw-Pitch 角度对当前面部识别系统的生物特征性能产生了重大影响。此外,我们提出了一个轻量级高效的姿态质量预测器,符合ISO/IEC WD 29794-5的标准,并在这个https URL上免费可用。
URL
https://arxiv.org/abs/2303.00491
