AI RESEARCH
On Applicability of Synthetic Datasets for Facial Expression Recognition
arXiv CS.CV
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ArXi:2605.17483v1 Announce Type: new Facial Expression Recognition faces two core challenges. The first is class imbalance in public datasets, which skews the learning process and weakens generalization. The second is related to privacy and data collection constraints, which limit the sharing of facial images and restrict the creation of large, balanced datasets. To address these issues, we examine three complementary strategies for constructing privacy-preserving FER datasets in the standard seven discrete facial expression classes setting.