{"ID":2884901,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.09202","arxiv_id":"2508.09202","title":"Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method","abstract":"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. We address a particularly challenging setting where source data is unavailable and the target data contains only neutral expressions. Existing SFDA methods are not designed for adaptation from a single target class, while generating non-neutral facial images is often unstable and expensive. To address this, we propose Source-Free Domain Adaptation with Personalized Feature Translation (SFDA-PFT), a lightweight latent-space approach. A translator is first pretrained on source data to map subject-specific style features between subjects while preserving expression information through expression-consistency and style-aware objectives. It is then adapted to neutral target data without source data or image synthesis. By operating in the latent space, SFDA-PFT avoids noisy facial image generation, reduces computation, and learns discriminative embeddings for classification. Experiments on BioVid, StressID, BAH, and Aff-Wild2 show that SFDA-PFT consistently outperforms state-of-the-art SFDA methods in privacy-sensitive FER scenarios. Our code is publicly available at: \\href{https://github.com/MasoumehSharafi/SFDA-PFT}{GitHub}.","short_abstract":"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. Sourc...","url_abs":"https://arxiv.org/abs/2508.09202","url_pdf":"https://arxiv.org/pdf/2508.09202v4","authors":"[\"Masoumeh Sharafi\",\"Soufiane Belharbi\",\"Muhammad Osama Zeeshan\",\"Houssem Ben Salem\",\"Ali Etemad\",\"Alessandro Lameiras Koerich\",\"Marco Pedersoli\",\"Simon Bacon\",\"Eric Granger\"]","published":"2025-08-08T20:13:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":611132,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2884901,"paper_url":"https://arxiv.org/abs/2508.09202","paper_title":"Personalized Feature Translation for Expression Recognition: An Efficient Source-Free Domain Adaptation Method","repo_url":"https://github.com/MasoumehSharafi/SFDA-PFT","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}