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Found 6 papers, 4 papers with code
AI-KD: Towards Alignment Invariant Face Image Quality Assessment Using Knowledge Distillation
To address this problem, we present in this paper a novel knowledge distillation approach, termed AI-KD that can extend on any existing FIQA technique, improving its robustness to alignment variations and, in turn, performance with different alignment procedures.
Optimization-Based Improvement of Face Image Quality Assessment Techniques
To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process.
DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
In this paper, we present a powerful new FIQA approach, named DifFIQA, which relies on denoising diffusion probabilistic models (DDPM) and ensures highly competitive results.
FaceQAN: Face Image Quality Assessment Through Adversarial Noise Exploration
In this paper, we propose a novel approach to face image quality assessment, called FaceQAN, that is based on adversarial examples and relies on the analysis of adversarial noise which can be calculated with any FR model learned by using some form of gradient descent.
Assessing Bias in Face Image Quality Assessment
Although modern face recognition systems are known to perform well, several studies have found that such systems often exhibit problems with demographic bias.
Iterative Optimization of Pseudo Ground-Truth Face Image Quality Labels
At ten iterations, the approach seems to perform the best, consistently outperforming the base quality scores of the three FIQA methods, chosen for the experiments.
