Improve face recognition by using image quality to mark hard examples with AdaFace
Improve face recognition by using image quality to mark hard examples with AdaFace
AdaFace: Quality Adaptive Margin for Face Recognition
arXiv paper abstract https://arxiv.org/abs/2204.00964v1
arXiv PDF paper https://arxiv.org/pdf/2204.00964v1.pdf
Recognition in low quality face datasets is challenging because facial attributes are obscured and degraded.
... previous studies have studied ... assign more importance to misclassified (hard) examples.
... argue ... the relative importance of easy or hard samples should be based on the sample's image quality.
... propose a new loss function that emphasizes samples of different difficulties based on their image quality.
... method achieves this in the form of an adaptive margin function by approximating the image quality with feature norms.
... show ... AdaFace, improves the face recognition performance over the state-of-the-art (SoTA) on four datasets (IJB-B, IJB-C, IJB-S and TinyFace). ...
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