Year: 2026
Type: article
Abstract: Facial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models. While psychological research suggests that expressions are often combinations of basic emotional units, most existing FER methods rely on deterministic point estimation, failing to model the intrinsic uncertainty and continuous nature of emotions. To address this, we propose POSTER-Var, a framework integrating a Variational Inference-based Classification Head (VICH). Unlike standard classifiers, VICH maps facial features into a probabilistic latent space via the reparameterization trick, enabling the model to learn the underlying distribution of expression intensities. Furthermore, we enhance feature representation by introducing layer embeddings and nonlinear transformations into the feature pyramid, facilitating the fusion of hierarchical semantic information. Extensive experiments on RAF-DB, AffectNet, and FER+ demonstrate that our method effectively handles fine-grained expression recognition, achieving state-of-the-art performance. The code has been open-sourced at: https://github.com/lg2578/poster-var .
Source: Scientific Reports
Authors: Gang Lv, JunLing Zhang, Chiki Tsoi
Institution: Peking University
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,690
Awards: G1901830537, G2413285440
OpenAlex ID: W7128038034