Group Emotion Recognition Using Fuzzy Emotional Hierarchical Shared Space

Group Emotion Recognition (GER) aims to estimate the collective affect displayed by multiple people in a shared scene. Hard-label voting and static fusion strategies used in prior work often overlook the intrinsic ambiguity of individual emotions and the non-linear group-level interactions, including conflict, tendency and resonance. We present Fuzzy Emotional Hierarchical Shared Space (FEHSS), a three-subspace framework that assigns each person a soft membership vector across emotion categories, simultaneously optimises conflict suppression, tendency regulation and resonance enhancement, and updates its fusion rules on the fly through a hierarchical reinforcement optimiser (HIRO). Comprehensive evaluations on GroupEmoW, GAD 2.0, and EMOTIC-7 confirm that FEHSS sets a new state of the art, reaching 91.77% accuracy on GroupEmoW and consistently outperforming previous methods on the other two benchmarks. Extensive ablation studies confirm the complementary roles of the three subspaces and the necessity of dynamic parameter tuning. Qualitative visualisations further illustrate how FEHSS resolves contradictory cues and preserves subtle shared emotions that rigid fusion schemes overlook. Additional experiments on the EMOTIC-7 dataset with seven emotion classes show that FEHSS attains 74.3% top-1 accuracy and 71.8% macro-F1. The proposed framework thus offers both higher accuracy and stronger interpretability, providing a new pathway toward robust GER in unconstrained real-world scenarios.