Face Quality Assessement
3 papers with code • 3 benchmarks • 3 datasets
Estimate the usability of a given face image for recognition
Face image quality is an important factor to enable high performance face recognition systems.
This paper proposes MagFace, a category of losses that learn a universal feature embedding whose magnitude can measure the quality of the given face.
Based on that, our proposed CR-FIQA uses this paradigm to estimate the face image quality of a sample by predicting its relative classifiability.