AI RESEARCH
Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method
arXiv CS.AI
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ArXi:2508.09202v3 Announce Type: replace-cross Facial expression recognition (FER) models are widely used in video-based affective computing applications, such as human-computer interaction and healthcare monitoring. However, deep FER models often struggle with subtle expressions and high inter-subject variability, limiting performance in real-world settings. Source-free domain adaptation (SFDA) has been proposed to personalize a pretrained source model using only unlabeled target data, avoiding privacy, storage, and transmission constraints.