4 accepted papers
Recent advancements in Face Image Quality Assessment (FIQA) models trained on real large-scale face datasets are pivotal in guaranteeing precise face recognition in unrestricted scenarios. Regrettably, privacy concerns lead to the discontinuation of real datasets, underscoring the pressing need for…
Face Image Quality Assessment (FIQA) is pivotal for guaranteeing the accuracy of face recognition in unconstrained environments. Recent progress in deep quality-fitting-based methods that train models to align with quality anchors has shown promise in FIQA. However these methods heavily depend on a…
Face Image Quality Assessment (FIQA) lays the foundation for ensuring the stability and accuracy of face recognition systems. However, existing FIQA methods mainly formulate quality relationships within the training set to yield quality scores, ignoring the generalization problem caused by ethnic qu…
In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and…