MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training

• URL: http://arxiv.org/abs/2508.12522v1
• Date: Sun, 17 Aug 2025 23:08:21 GMT
• Title: MuSACo: Multimodal Subject-Specific Selection and Adaptation for Expression Recognition with Co-Training
• Authors: Muhammad Osama Zeeshan, Natacha Gillet, Alessandro Lameiras Koerich, Marco Pedersoli, Francois Bremond, Eric Granger,
• Abstract summary: We introduce MuSACo, a multi-modal subject-specific selection and adaptation method for personalized expression recognition.<n>This makes MuSACo relevant for affective computing applications in digital health, such as patient-specific assessment for stress or pain.<n>Our experimental results on challenging multimodal ER datasets: BioVid and StressID, show that MuSACo can outperform UDA (blending) and state-of-the-art MSDA methods.
• Score: 52.99217736494484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Personalized expression recognition (ER) involves adapting a machine learning model to subject-specific data for improved recognition of expressions with considerable interpersonal variability. Subject-specific ER can benefit significantly from multi-source domain adaptation (MSDA) methods, where each domain corresponds to a specific subject, to improve model accuracy and robustness. Despite promising results, state-of-the-art MSDA approaches often overlook multimodal information or blend sources into a single domain, limiting subject diversity and failing to explicitly capture unique subject-specific characteristics. To address these limitations, we introduce MuSACo, a multi-modal subject-specific selection and adaptation method for ER based on co-training. It leverages complementary information across multiple modalities and multiple source domains for subject-specific adaptation. This makes MuSACo particularly relevant for affective computing applications in digital health, such as patient-specific assessment for stress or pain, where subject-level nuances are crucial. MuSACo selects source subjects relevant to the target and generates pseudo-labels using the dominant modality for class-aware learning, in conjunction with a class-agnostic loss to learn from less confident target samples. Finally, source features from each modality are aligned, while only confident target features are combined. Our experimental results on challenging multimodal ER datasets: BioVid and StressID, show that MuSACo can outperform UDA (blending) and state-of-the-art MSDA methods.

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