Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals
This work addresses emotion recognition for applications like human-computer interaction, but it is incremental as it builds on existing multimodal approaches.
The paper tackled latent emotion recognition by combining micro-expression and physiological signals, achieving improved accuracy over benchmark methods through a novel multimodal learning framework.
This paper discusses the benefits of incorporating multimodal data for improving latent emotion recognition accuracy, focusing on micro-expression (ME) and physiological signals (PS). The proposed approach presents a novel multimodal learning framework that combines ME and PS, including a 1D separable and mixable depthwise inception network, a standardised normal distribution weighted feature fusion method, and depth/physiology guided attention modules for multimodal learning. Experimental results show that the proposed approach outperforms the benchmark method, with the weighted fusion method and guided attention modules both contributing to enhanced performance.