Multimodal Latent Emotion Recognition from Micro-expression and
  Physiological Signals
Liangfei Zhang Yifei Qian Ognjen Arandjelovic Anthony Zhu
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.