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arXiv · 2506.19079

Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition

Iosif Tsangko·Andreas Triantafyllopoulos·Adem Abdelmoula·Adria Mallol-Ragolta·Bjoern W. Schuller
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Abstract

Foundation Models (FMs) are rapidly transforming Affective Computing (AC), with Vision Language Models (VLMs) now capable of recognising emotions in zero shot settings. This paper probes a critical but underexplored question: what visual cues do these models rely on to infer affect, and are these cues psychologically grounded or superficially learnt? We benchmark varying scale VLMs on a teeth annotated subset of AffectNet dataset and find consistent performance shifts depending on the presence of visible teeth. Through structured introspection of, the best-performing model, i.e., GPT-4o, we show that facial attributes like eyebrow position drive much of its affective reasoning, revealing a high degree of internal consistency in its valence-arousal predictions. These patterns highlight the emergent nature of FMs behaviour, but also reveal risks: shortcut learning, bias, and fairness issues especially in sensitive domains like mental health and education.

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Iosif Tsangko, Andreas Triantafyllopoulos, Adem Abdelmoula, Adria Mallol-Ragolta, Bjoern W. Schuller. 2025-06-23. Reading Smiles: Proxy Bias in Foundation Models for Facial Emotion Recognition. https://doi.org/10.1109/access.2025.3636968

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