Pose Impact Estimation on Face Recognition using 3D-Aware Synthetic Data with Application to Quality Assessment
This work addresses the need for standardized quality assessment in face recognition systems, particularly for operators to recapture low-quality images, though it is incremental as it builds on existing standards and methods.
The authors tackled the problem of evaluating facial image quality for face recognition systems by creating a synthetic dataset (Syn-YawPitch) with 1000 identities and varying yaw-pitch angles, demonstrating that pitch angles beyond 30 degrees significantly impact biometric performance. They also proposed a lightweight, explainable pose quality predictor that adheres to ISO/IEC CD3 29794-5 and benchmarked it against state-of-the-art algorithms.
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