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This paper discusses the benefits of incorporating multimodal data forimproving latent emotion recognition accuracy, focusing on micro-expression(ME) and physiological signals (PS). The proposed approach presents a novelmultimodal learning framework that combines ME and PS, including a 1D separableand mixable depthwise inception network, a standardised normal distributionweighted feature fusion method, and depth/physiology guided attention modulesfor multimodal learning. Experimental results show that the proposed approachoutperforms the benchmark method, with the weighted fusion method and guidedattention modules both contributing to enhanced performance.

Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals