A Novel Weber Cross Information Sharing Deep Learning Encoder Decoder Model for Emotion Recognition Using Facial Expression

This study introduces an innovative deep learning framework, the Weber Cross Information Sharing Deep Learning Encoder-Decoder (WCISD-ED) model, designed for emotion recognition through facial expression analysis. Recognition of emotion is a pivotal aspect of man-machine interaction, offering profound implications in areas ranging from mental health assessment to customer service and entertainment. However, because human expressions are so subtle and varied, accurately deducing emotions from facial expressions is a sophisticated task. The WCISD-ED model is crafted to address these complexities by incorporating principles derived from Weber's Law, which relates to the perception of changes in visual stimuli. This integration enhances the model's sensitivity to the minute yet critical variations in facial expressions associated with different emotions. The model features a novel cross information sharing structure within an encoder-decoder architecture, enabling the effective processing of facial features at multiple scales and depths. The encoder segment of the model focuses on the detailed extraction of facial features, while the decoder reconstructs these features into recognizable emotion categories. The cross information sharing mechanism allows for the interaction between different layers of the network, facilitating a more comprehensive and nuanced understanding of facial expressions. Extensive testing on diverse datasets demonstrates that the WCISD-ED model significantly outperforms existing emotion recognition models in terms of accuracy and reliability.