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

TL;DR

This paper introduces a new 3D-aware synthetic face dataset to analyze how pose variations affect face recognition accuracy and proposes an explainable pose quality predictor aligned with international standards.

Contribution

The paper presents Syn-YawPitch, a synthetic dataset for pose impact analysis, and develops a lightweight, explainable pose quality predictor compliant with ISO standards.

Findings

Pitch angles over 30 degrees significantly reduce recognition accuracy.

The proposed predictor outperforms existing face quality assessment algorithms.

Pose variation is a critical factor in face recognition performance.

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 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