Towards Robust Open-World Emotion Recognition via Equiangular Prototypes and Progressive Prediction

Open-world emotion recognition aims to perform emotion classification using both labeled and unlabeled data, where the labeled data cover only a subset of emotion classes, while the unlabeled data encompass all classes. This setting is crucial for recognizing fine-grained unseen emotions that are difficult to collect under controlled laboratory conditions. However, existing open-world semi-supervised learning methods suffer from two major limitations: (1) they rely on similarity-based clustering for unconstrained aggregation, making it difficult to distinguish representations of different unseen classes in the absence of supervision for unseen classes; and (2) they primarily rely on the predictive confidence of a single model for class decisions, making them susceptible to distributional bias dominated by known classes and consequently prone to biased predictions. To address these issues, we propose a novel open-world emotion recognition framework, termed ROBIN. First, inspired by neural collapse, we introduce the simplex equiangular tight frame structure as equiangular prototypes to guide representations of different classes toward a well-separated distributional structure. Subsequently, we develop a progressive prediction strategy that first employs an auxiliary binary classifier to distinguish between known and unseen classes, and then masks class components inconsistent with the predicted base class, thereby enabling robust fine-grained class recognition. Extensive experimental results demonstrate that ROBIN outperforms existing state-of-the-art methods on the open-world emotion recognition.