Paper ID: 2412.01860 • Published Dec 1, 2024
Pairwise Discernment of AffectNet Expressions with ArcFace
Dylan Waldner, Shyamal Mitra
TL;DR
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This study takes a preliminary step toward teaching computers to recognize
human emotions through Facial Emotion Recognition (FER). Transfer learning is
applied using ResNeXt, EfficientNet models, and an ArcFace model originally
trained on the facial verification task, leveraging the AffectNet database, a
collection of human face images annotated with corresponding emotions. The
findings highlight the value of congruent domain transfer learning, the
challenges posed by imbalanced datasets in learning facial emotion patterns,
and the effectiveness of pairwise learning in addressing class imbalances to
enhance model performance on the FER task.