Age-aware loss reweighting and age-conditioned training reduce elderly recognition errors in facial expression models, even when age labels are automatically estimated, but the evidence rests on a small elderly benchmark.
Fully automated age-weighted expression classification using real and apparent age
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Bridging the gap in FER: addressing age bias in deep learning
Age-aware loss reweighting and age-conditioned training reduce elderly recognition errors in facial expression models, even when age labels are automatically estimated, but the evidence rests on a small elderly benchmark.