Face Quality Assessement
3 papers with code • 3 benchmarks • 3 datasets
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Most implemented papers
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face recognition systems.
MagFace: A Universal Representation for Face Recognition and Quality Assessment
This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face.
CR-FIQA: Face Image Quality Assessment by Learning Sample Relative Classifiability
Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability.