Emotion recognition has become increasingly significant in artificial intelligence; however, the impact of body movements on emotion interpretation remains under-explored. This paper presents a novel Hybrid Bayesian Pre-trained Long Short-Term Memory (HBP-LSTM) framework that combines low-level pose data with high-level kinematic features, utilising Bayesian inference to enhance the accuracy and robustness of emotion recognition. The proposed model is trained on high-quality laboratory data to capture the fundamental patterns of emotional expression through body movements. We introduce noise and employ adversarial attack methods such as the Fast Gradient Sign Method (FGSM) to evaluate the model’s robustness during testing. This approach assesses the HBP-LSTM’s ability to maintain performance under data degradation and adversarial conditions, common challenges in real-world scenarios. We validated the HBP-LSTM on two public datasets, EGBM and KDAEE, demonstrating that the model exhibits high robustness against noise and adversarial perturbations, outperforming traditional models. The HBP-LSTM accurately identifies seven basic emotions (happiness, sadness, surprise, fear, anger, disgust, and neutrality) with accuracies of 98% and 88% on the EGBM and KDAEE datasets, respectively. HBP-LSTM is a noise-resistant model with a reliable emotion recognition framework, which lays the foundation for future applications of emotion recognition technology in more challenging real-world environments.
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